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ehdwns1516/gpt3-kor-based_gpt2_review_SR2 | ehdwns1516 | 2021-07-23T01:16:21Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | # ehdwns1516/gpt3-kor-based_gpt2_review_SR2
* This model has been trained Korean dataset as a star of 2 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
## Model links for each 1 to 5 star
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)
## Overview
Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2)
Language: Korean
Training data: review_body dataset with a star of 2 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR2")
model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR2")
generator = pipeline(
"text-generation",
model="ehdwns1516/gpt3-kor-based_gpt2_review_SR2",
tokenizer=tokenizer
)
context = "your context"
result = dict()
result[0] = generator(context)[0]
```
|
ehdwns1516/gpt2_review_star5 | ehdwns1516 | 2021-07-23T01:07:44Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | # gpt2_review_star5
* This model has been trained as a review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
## Model links for each 1 to 5 star
* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)
## Overview
Language model: [gpt2](https://huggingface.co/gpt2)
Language: English
Training data: review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star5")
model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star5")
generator = pipeline(
"text-generation",
model="ehdwns1516/gpt2_review_star5",
tokenizer=tokenizer
)
context = "your context"
result = dict()
result[0] = generator(context)[0]
```
|
ehdwns1516/gpt2_review_star2 | ehdwns1516 | 2021-07-23T01:06:41Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | # gpt2_review_star2
* This model has been trained as a review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
## Model links for each 1 to 5 star
* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)
## Overview
Language model: [gpt2](https://huggingface.co/gpt2)
Language: English
Training data: review_body dataset with a star of 2 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star2")
model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star2")
generator = pipeline(
"text-generation",
model="ehdwns1516/gpt2_review_star2",
tokenizer=tokenizer
)
context = "your context"
result = dict()
result[0] = generator(context)[0]
```
|
Fraser/wiki-vae | Fraser | 2021-07-22T19:16:20Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z |
# Wiki-VAE
A Transformer-VAE trained on all the sentences in wikipedia.
Training is done on AWS SageMaker.
|
tylerroofingcompany/newwebsite | tylerroofingcompany | 2021-07-22T06:45:54Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | Hugging Face's logo
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aristotletan/bart-large-finetuned-xsum | aristotletan | 2021-07-22T01:45:40Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:wsj_markets",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- wsj_markets
metrics:
- rouge
model_index:
- name: bart-large-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wsj_markets
type: wsj_markets
args: default
metric:
name: Rouge1
type: rouge
value: 15.3934
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-finetuned-xsum
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the wsj_markets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8497
- Rouge1: 15.3934
- Rouge2: 7.0378
- Rougel: 13.9522
- Rougelsum: 14.3541
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0964 | 1.0 | 1735 | 0.9365 | 18.703 | 12.7539 | 18.1293 | 18.5397 | 20.0 |
| 0.95 | 2.0 | 3470 | 0.8871 | 19.5223 | 13.0938 | 18.9148 | 18.8363 | 20.0 |
| 0.8687 | 3.0 | 5205 | 0.8587 | 15.0915 | 7.142 | 13.6693 | 14.5975 | 20.0 |
| 0.7989 | 4.0 | 6940 | 0.8569 | 18.243 | 11.4495 | 17.4326 | 17.489 | 20.0 |
| 0.7493 | 5.0 | 8675 | 0.8497 | 15.3934 | 7.0378 | 13.9522 | 14.3541 | 20.0 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.10.0
- Tokenizers 0.10.3
|
aristotletan/t5-small-finetuned-xsum | aristotletan | 2021-07-22T00:18:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wsj_markets",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wsj_markets
metrics:
- rouge
model_index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wsj_markets
type: wsj_markets
args: default
metric:
name: Rouge1
type: rouge
value: 10.4492
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wsj_markets dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1447
- Rouge1: 10.4492
- Rouge2: 3.9563
- Rougel: 9.3368
- Rougelsum: 9.9828
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 2.2742 | 1.0 | 868 | 1.3135 | 9.4644 | 2.618 | 8.4048 | 8.9764 | 19.0 |
| 1.4607 | 2.0 | 1736 | 1.2134 | 9.6327 | 3.8535 | 9.0703 | 9.2466 | 19.0 |
| 1.3579 | 3.0 | 2604 | 1.1684 | 10.1616 | 3.5498 | 9.2294 | 9.4507 | 19.0 |
| 1.3314 | 4.0 | 3472 | 1.1514 | 10.0621 | 3.6907 | 9.1635 | 9.4955 | 19.0 |
| 1.3084 | 5.0 | 4340 | 1.1447 | 10.4492 | 3.9563 | 9.3368 | 9.9828 | 19.0 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.10.0
- Tokenizers 0.10.3
|
huggingtweets/devops_guru-neiltyson-nigelthurlow | huggingtweets | 2021-07-21T22:55:43Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/devops_guru-neiltyson-nigelthurlow/1626908139492/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1163117736140124160/u23u5DU4_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/748969887146471424/4BmVTQAv_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/74188698/NeilTysonOriginsA-Crop_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Nigel Thurlow & Ernest Wright, Ph. D. ABD & Neil deGrasse Tyson</div>
<div style="text-align: center; font-size: 14px;">@devops_guru-neiltyson-nigelthurlow</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Nigel Thurlow & Ernest Wright, Ph. D. ABD & Neil deGrasse Tyson.
| Data | Nigel Thurlow | Ernest Wright, Ph. D. ABD | Neil deGrasse Tyson |
| --- | --- | --- | --- |
| Tweets downloaded | 1264 | 1933 | 3250 |
| Retweets | 648 | 20 | 10 |
| Short tweets | 27 | 105 | 79 |
| Tweets kept | 589 | 1808 | 3161 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jc9vah1k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @devops_guru-neiltyson-nigelthurlow's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2myicem9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2myicem9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/devops_guru-neiltyson-nigelthurlow')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/nigelthurlow | huggingtweets | 2021-07-21T22:34:57Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/nigelthurlow/1626906893945/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1163117736140124160/u23u5DU4_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Nigel Thurlow</div>
<div style="text-align: center; font-size: 14px;">@nigelthurlow</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Nigel Thurlow.
| Data | Nigel Thurlow |
| --- | --- |
| Tweets downloaded | 1264 |
| Retweets | 648 |
| Short tweets | 27 |
| Tweets kept | 589 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/n4jwj2tf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nigelthurlow's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nigelthurlow')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AIDA-UPM/MSTSb_stsb-xlm-r-multilingual | AIDA-UPM | 2021-07-21T18:32:31Z | 54 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:04Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1438 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 4e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
flax-community/papuGaPT2 | flax-community | 2021-07-21T15:46:46Z | 1,172 | 10 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"text-generation",
"pl",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: pl
tags:
- text-generation
widget:
- text: "Najsmaczniejszy polski owoc to"
---
# papuGaPT2 - Polish GPT2 language model
[GPT2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) was released in 2019 and surprised many with its text generation capability. However, up until very recently, we have not had a strong text generation model in Polish language, which limited the research opportunities for Polish NLP practitioners. With the release of this model, we hope to enable such research.
Our model follows the standard GPT2 architecture and training approach. We are using a causal language modeling (CLM) objective, which means that the model is trained to predict the next word (token) in a sequence of words (tokens).
## Datasets
We used the Polish subset of the [multilingual Oscar corpus](https://www.aclweb.org/anthology/2020.acl-main.156) to train the model in a self-supervised fashion.
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_pl')
```
## Intended uses & limitations
The raw model can be used for text generation or fine-tuned for a downstream task. The model has been trained on data scraped from the web, and can generate text containing intense violence, sexual situations, coarse language and drug use. It also reflects the biases from the dataset (see below for more details). These limitations are likely to transfer to the fine-tuned models as well. At this stage, we do not recommend using the model beyond research.
## Bias Analysis
There are many sources of bias embedded in the model and we caution to be mindful of this while exploring the capabilities of this model. We have started a very basic analysis of bias that you can see in [this notebook](https://huggingface.co/flax-community/papuGaPT2/blob/main/papuGaPT2_bias_analysis.ipynb).
### Gender Bias
As an example, we generated 50 texts starting with prompts "She/He works as". The image below presents the resulting word clouds of female/male professions. The most salient terms for male professions are: teacher, sales representative, programmer. The most salient terms for female professions are: model, caregiver, receptionist, waitress.
### Ethnicity/Nationality/Gender Bias
We generated 1000 texts to assess bias across ethnicity, nationality and gender vectors. We created prompts with the following scheme:
* Person - in Polish this is a single word that differentiates both nationality/ethnicity and gender. We assessed the following 5 nationalities/ethnicities: German, Romani, Jewish, Ukrainian, Neutral. The neutral group used generic pronounts ("He/She").
* Topic - we used 5 different topics:
* random act: *entered home*
* said: *said*
* works as: *works as*
* intent: Polish *niech* which combined with *he* would roughly translate to *let him ...*
* define: *is*
Each combination of 5 nationalities x 2 genders x 5 topics had 20 generated texts.
We used a model trained on [Polish Hate Speech corpus](https://huggingface.co/datasets/hate_speech_pl) to obtain the probability that each generated text contains hate speech. To avoid leakage, we removed the first word identifying the nationality/ethnicity and gender from the generated text before running the hate speech detector.
The following tables and charts demonstrate the intensity of hate speech associated with the generated texts. There is a very clear effect where each of the ethnicities/nationalities score higher than the neutral baseline.
Looking at the gender dimension we see higher hate score associated with males vs. females.
We don't recommend using the GPT2 model beyond research unless a clear mitigation for the biases is provided.
## Training procedure
### Training scripts
We used the [causal language modeling script for Flax](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_clm_flax.py). We would like to thank the authors of that script as it allowed us to complete this training in a very short time!
### Preprocessing and Training Details
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.
We have trained the model on a single TPUv3 VM, and due to unforeseen events the training run was split in 3 parts, each time resetting from the final checkpoint with a new optimizer state:
1. LR 1e-3, bs 64, linear schedule with warmup for 1000 steps, 10 epochs, stopped after 70,000 steps at eval loss 3.206 and perplexity 24.68
2. LR 3e-4, bs 64, linear schedule with warmup for 5000 steps, 7 epochs, stopped after 77,000 steps at eval loss 3.116 and perplexity 22.55
3. LR 2e-4, bs 64, linear schedule with warmup for 5000 steps, 3 epochs, stopped after 91,000 steps at eval loss 3.082 and perplexity 21.79
## Evaluation results
We trained the model on 95% of the dataset and evaluated both loss and perplexity on 5% of the dataset. The final checkpoint evaluation resulted in:
* Evaluation loss: 3.082
* Perplexity: 21.79
## How to use
You can use the model either directly for text generation (see example below), by extracting features, or for further fine-tuning. We have prepared a notebook with text generation examples [here](https://huggingface.co/flax-community/papuGaPT2/blob/main/papuGaPT2_text_generation.ipynb) including different decoding methods, bad words suppression, few- and zero-shot learning demonstrations.
### Text generation
Let's first start with the text-generation pipeline. When prompting for the best Polish poet, it comes up with a pretty reasonable text, highlighting one of the most famous Polish poets, Adam Mickiewicz.
```python
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='flax-community/papuGaPT2')
set_seed(42)
generator('Największym polskim poetą był')
>>> [{'generated_text': 'Największym polskim poetą był Adam Mickiewicz - uważany za jednego z dwóch geniuszów języka polskiego. "Pan Tadeusz" był jednym z najpopularniejszych dzieł w historii Polski. W 1801 został wystawiony publicznie w Teatrze Wilama Horzycy. Pod jego'}]
```
The pipeline uses `model.generate()` method in the background. In [our notebook](https://huggingface.co/flax-community/papuGaPT2/blob/main/papuGaPT2_text_generation.ipynb) we demonstrate different decoding methods we can use with this method, including greedy search, beam search, sampling, temperature scaling, top-k and top-p sampling. As an example, the below snippet uses sampling among the 50 most probable tokens at each stage (top-k) and among the tokens that jointly represent 95% of the probability distribution (top-p). It also returns 3 output sequences.
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
model = AutoModelWithLMHead.from_pretrained('flax-community/papuGaPT2')
tokenizer = AutoTokenizer.from_pretrained('flax-community/papuGaPT2')
set_seed(42) # reproducibility
input_ids = tokenizer.encode('Największym polskim poetą był', return_tensors='pt')
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=50,
top_k=50,
top_p=0.95,
num_return_sequences=3
)
print("Output:\
" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))
>>> Output:
>>> ----------------------------------------------------------------------------------------------------
>>> 0: Największym polskim poetą był Roman Ingarden. Na jego wiersze i piosenki oddziaływały jego zamiłowanie do przyrody i przyrody. Dlatego też jako poeta w czasie pracy nad utworami i wierszami z tych wierszy, a następnie z poezji własnej - pisał
>>> 1: Największym polskim poetą był Julian Przyboś, którego poematem „Wierszyki dla dzieci”.
>>> W okresie międzywojennym, pod hasłem „Papież i nie tylko” Polska, jak większość krajów europejskich, była państwem faszystowskim.
>>> Prócz
>>> 2: Największym polskim poetą był Bolesław Leśmian, który był jego tłumaczem, a jego poezja tłumaczyła na kilkanaście języków.
>>> W 1895 roku nakładem krakowskiego wydania "Scientio" ukazała się w języku polskim powieść W krainie kangurów
```
### Avoiding Bad Words
You may want to prevent certain words from occurring in the generated text. To avoid displaying really bad words in the notebook, let's pretend that we don't like certain types of music to be advertised by our model. The prompt says: *my favorite type of music is*.
```python
input_ids = tokenizer.encode('Mój ulubiony gatunek muzyki to', return_tensors='pt')
bad_words = [' disco', ' rock', ' pop', ' soul', ' reggae', ' hip-hop']
bad_word_ids = []
for bad_word in bad_words:
ids = tokenizer(bad_word).input_ids
bad_word_ids.append(ids)
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=20,
top_k=50,
top_p=0.95,
num_return_sequences=5,
bad_words_ids=bad_word_ids
)
print("Output:\
" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))
>>> Output:
>>> ----------------------------------------------------------------------------------------------------
>>> 0: Mój ulubiony gatunek muzyki to muzyka klasyczna. Nie wiem, czy to kwestia sposobu, w jaki gramy,
>>> 1: Mój ulubiony gatunek muzyki to reggea. Zachwycają mnie piosenki i piosenki muzyczne o ducho
>>> 2: Mój ulubiony gatunek muzyki to rockabilly, ale nie lubię też punka. Moim ulubionym gatunkiem
>>> 3: Mój ulubiony gatunek muzyki to rap, ale to raczej się nie zdarza w miejscach, gdzie nie chodzi
>>> 4: Mój ulubiony gatunek muzyki to metal aranżeje nie mam pojęcia co mam robić. Co roku,
```
Ok, it seems this worked: we can see *classical music, rap, metal* among the outputs. Interestingly, *reggae* found a way through via a misspelling *reggea*. Take it as a caution to be careful with curating your bad word lists!
### Few Shot Learning
Let's see now if our model is able to pick up training signal directly from a prompt, without any finetuning. This approach was made really popular with GPT3, and while our model is definitely less powerful, maybe it can still show some skills! If you'd like to explore this topic in more depth, check out [the following article](https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api) which we used as reference.
```python
prompt = """Tekst: "Nienawidzę smerfów!"
Sentyment: Negatywny
###
Tekst: "Jaki piękny dzień 👍"
Sentyment: Pozytywny
###
Tekst: "Jutro idę do kina"
Sentyment: Neutralny
###
Tekst: "Ten przepis jest świetny!"
Sentyment:"""
res = generator(prompt, max_length=85, temperature=0.5, end_sequence='###', return_full_text=False, num_return_sequences=5,)
for x in res:
print(res[i]['generated_text'].split(' ')[1])
>>> Pozytywny
>>> Pozytywny
>>> Pozytywny
>>> Pozytywny
>>> Pozytywny
```
It looks like our model is able to pick up some signal from the prompt. Be careful though, this capability is definitely not mature and may result in spurious or biased responses.
### Zero-Shot Inference
Large language models are known to store a lot of knowledge in its parameters. In the example below, we can see that our model has learned the date of an important event in Polish history, the battle of Grunwald.
```python
prompt = "Bitwa pod Grunwaldem miała miejsce w roku"
input_ids = tokenizer.encode(prompt, return_tensors='pt')
# activate beam search and early_stopping
beam_outputs = model.generate(
input_ids,
max_length=20,
num_beams=5,
early_stopping=True,
num_return_sequences=3
)
print("Output:\
" + 100 * '-')
for i, sample_output in enumerate(beam_outputs):
print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))
>>> Output:
>>> ----------------------------------------------------------------------------------------------------
>>> 0: Bitwa pod Grunwaldem miała miejsce w roku 1410, kiedy to wojska polsko-litewskie pod
>>> 1: Bitwa pod Grunwaldem miała miejsce w roku 1410, kiedy to wojska polsko-litewskie pokona
>>> 2: Bitwa pod Grunwaldem miała miejsce w roku 1410, kiedy to wojska polsko-litewskie,
```
## BibTeX entry and citation info
```bibtex
@misc{papuGaPT2,
title={papuGaPT2 - Polish GPT2 language model},
url={https://huggingface.co/flax-community/papuGaPT2},
author={Wojczulis, Michał and Kłeczek, Dariusz},
year={2021}
}
``` |
ktangri/gpt-neo-demo | ktangri | 2021-07-21T15:20:09Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"the Pile",
"causal-lm",
"en",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- text generation
- pytorch
- the Pile
- causal-lm
license: apache-2.0
datasets:
- the Pile
---
# GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.
## Training procedure
This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
## Intended Use and Limitations
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
>>> generator("EleutherAI has", do_sample=True, min_length=50)
[{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
```
### Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Eval results
All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM).
### Linguistic Reasoning
| Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag |
| ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- |
| GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 | 7.498 | 57.23% | 55.01% | 38.66% |
| GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% |
| **GPT-Neo 2.7B** | **0.7165** | **5.646** | **11.39** | **5.626** | **62.22%** | **56.50%** | **42.73%** |
| GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% |
### Physical and Scientific Reasoning
| Model and Size | MathQA | PubMedQA | Piqa |
| ---------------- | ---------- | ---------- | ----------- |
| GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% |
| GPT-2 1.5B | 23.64% | 58.33% | 70.78% |
| **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%** |
| GPT-3 Ada | 24.29% | 52.80% | 68.88% |
### Down-Stream Applications
TBD
### BibTeX entry and citation info
To cite this model, use
```bibtex
@article{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
To cite the codebase that this model was trained with, use
```bibtex
@software{gpt-neo,
author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella},
title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow},
url = {http://github.com/eleutherai/gpt-neo},
version = {1.0},
year = {2021},
}
``` |
ifis-zork/ZORK_AI_SCI_FI | ifis-zork | 2021-07-21T14:18:15Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_SCI_FI
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_SCI_FI
This model is a fine-tuned version of [ifis-zork/ZORK_AI_SCI_FI_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_SCI_FI_TEMP) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
bipin/malayalam-news-classifier | bipin | 2021-07-21T13:40:25Z | 9 | 3 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"malayalam",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- text-classification
- roberta
- malayalam
- pytorch
widget:
- text: "2032 ഒളിമ്പിക്സിന് ബ്രിസ്ബെയ്ന് വേദിയാകും; ഗെയിംസിന് വേദിയാകുന്ന മൂന്നാമത്തെ ഓസ്ട്രേലിയന് നഗരം"
---
## Malayalam news classifier
### Overview
This model is trained on top of [MalayalamBert](https://huggingface.co/eliasedwin7/MalayalamBERT) for the task of classifying malayalam news headlines. Presently, the following news categories are supported:
* Business
* Sports
* Entertainment
### Dataset
The dataset used for training this model can be found [here](https://www.kaggle.com/disisbig/malyalam-news-dataset).
### Using the model with HF pipeline
```python
from transformers import pipeline
news_headline = "ക്രിപ്റ്റോ ഇടപാടുകളുടെ വിവരങ്ങൾ ആവശ്യപ്പെട്ട് ആദായനികുതി വകുപ്പ് നോട്ടീസയച്ചു"
model = pipeline(task="text-classification", model="bipin/malayalam-news-classifier")
model(news_headline)
# Output
# [{'label': 'business', 'score': 0.9979357123374939}]
```
### Contact
For feedback and questions, feel free to contact via twitter [@bkrish_](https://twitter.com/bkrish_) |
mshamrai/bert-base-ukr-eng-rus-uncased | mshamrai | 2021-07-21T12:05:26Z | 38 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | This repository shares smaller version of bert-base-multilingual-uncased that keeps only Ukrainian, English, and Russian tokens in the vocabulary.
| Model | Num parameters | Size |
| ----------------------------------------- | -------------- | --------- |
| bert-base-multilingual-uncased | 167 million | ~650 MB |
| MaxVortman/bert-base-ukr-eng-rus-uncased | 110 million | ~423 MB | |
defex/distilgpt2-finetuned-amazon-reviews | defex | 2021-07-21T10:36:15Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-amazon-reviews
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-amazon-reviews
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
|
ifis-zork/ZORK_AI_FANTASY | ifis-zork | 2021-07-21T09:50:17Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_FANTASY
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_FANTASY
This model is a fine-tuned version of [ifis-zork/ZORK_AI_FAN_TEMP](https://huggingface.co/ifis-zork/ZORK_AI_FAN_TEMP) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
flax-community/clip-vision-bert-cc12m-60k | flax-community | 2021-07-21T09:17:15Z | 9 | 2 | transformers | [
"transformers",
"jax",
"clip-vision-bert",
"fill-mask",
"arxiv:1908.03557",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # CLIP-Vision-BERT Multilingual Pre-trained Model
Pretrained CLIP-Vision-BERT pre-trained on translated [Conceptual-12M](https://github.com/google-research-datasets/conceptual-12m) image-text pairs using a masked language modeling (MLM) objective. 10M cleaned image-text pairs are translated using [mBART-50 one-to-many model](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) to 2.5M examples each in English, French, German and Spanish. This model is based on the VisualBERT which was introduced in
[this paper](https://arxiv.org/abs/1908.03557) and first released in
[this repository](https://github.com/uclanlp/visualbert). We trained CLIP-Vision-BERT model during community week hosted by Huggingface 🤗 using JAX/Flax.
This checkpoint is pre-trained for 60k steps.
## Model description
CLIP-Vision-BERT is a modified BERT model which takes in visual embeddings from CLIP-Vision transformer and concatenates them with BERT textual embeddings before passing them to the self-attention layers of BERT. This is done for deep cross-modal interaction between the two modes.
## Intended uses & limitations❗️
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks such as visuo-linguistic sequence classification or visual question answering. We used this model to fine-tuned on a multi-translated version of the visual question answering task - [VQA v2](https://visualqa.org/challenge.html). Since Conceptual-12M is a dataset scraped from the internet, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly with a pipeline for masked language modeling. You will need to clone the model from [here](https://github.com/gchhablani/multilingual-vqa). An example of usage is shown below:
```python
>>> from torchvision.io import read_image
>>> import numpy as np
>>> import os
>>> from transformers import CLIPProcessor, BertTokenizerFast
>>> from model.flax_clip_vision_bert.modeling_clip_vision_bert import FlaxCLIPVisionBertForMaskedLM
>>> image_path = os.path.join('images/val2014', os.listdir('images/val2014')[0])
>>> img = read_image(image_path)
>>> clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.
>>> clip_outputs = clip_processor(images=img)
>>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images.
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
>>> model = FlaxCLIPVisionBertForMaskedLM.from_pretrained('flax-community/clip-vision-bert-cc12m-60k')
>>> text = "Three teddy [MASK] in a showcase."
>>> tokens = tokenizer([text], return_tensors="np")
>>> pixel_values = np.concatenate([clip_outputs['pixel_values']])
>>> outputs = model(pixel_values=pixel_values, **tokens)
>>> indices = np.where(tokens['input_ids']==tokenizer.mask_token_id)
>>> preds = outputs.logits[indices][0]
>>> sorted_indices = np.argsort(preds)[::-1] # Get reverse sorted scores
/home/crocoder/anaconda3/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:4615: UserWarning: 'kind' argument to argsort is ignored.
warnings.warn("'kind' argument to argsort is ignored.")
>>> top_5_indices = sorted_indices[:5]
>>> top_5_tokens = tokenizer.convert_ids_to_tokens(top_5_indices)
>>> top_5_scores = preds[top_5_indices]
>>> print(dict(zip(top_5_tokens, top_5_scores)))
{'bears': 19.241959, 'bear': 17.700356, 'animals': 14.368396, 'girls': 14.343797, 'dolls': 14.274415}
```
## Training data 🏋🏻♂️
The CLIP-Vision-BERT model was pre-trained on a translated version of the Conceptual-12m dataset in four languages using mBART-50: English, French, German and Spanish, with 2.5M image-text pairs in each.
The dataset captions and image urls can be downloaded from [flax-community/conceptual-12m-mbart-50-translated](https://huggingface.co/datasets/flax-community/conceptual-12m-mbart-50-multilingual).
## Data Cleaning 🧹
Though the original dataset contains 12M image-text pairs, a lot of the URLs are invalid now, and in some cases, images are corrupt or broken. We remove such examples from our data, which leaves us with approximately 10M image-text pairs.
**Splits**
We used 99% of the 10M examples as a train set, and the remaining ~ 100K examples as our validation set.
## Training procedure 👨🏻💻
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of approximately 110,000. The beginning of a new document is marked with `[CLS]` and the end of one by `[SEP]`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The visual embeddings are taken from the CLIP-Vision model and combined with the textual embeddings inside the BERT embedding layer. The padding is done in the middle. Here is an example of what the embeddings look like:
```
[CLS Emb] [Textual Embs] [SEP Emb] [Pad Embs] [Visual Embs]
```
A total length of 128 tokens, including the visual embeddings, is used. The texts are truncated or padded accordingly.
### Pretraining
The checkpoint of the model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 60k steps with a per device batch size of 64 and a max sequence length of 128. The optimizer used is Adafactor with a learning rate of 1e-4, learning rate warmup for 5,000 steps, and linear decay of the learning rate after.
We tracked experiments using TensorBoard. Here is the link to the main dashboard: [CLIP Vision BERT CC12M Pre-training Dashboard](https://huggingface.co/flax-community/multilingual-vqa-pt-ckpts/tensorboard)
#### **Pretraining Results 📊**
The model at this checkpoint reached **eval accuracy of 67.53%** and **with train loss at 1.793 and eval loss at 1.724**.
## Fine Tuning on downstream tasks
We performed fine-tuning on downstream tasks. We used the following datasets for visual question answering:
1. Multilingual of [Visual Question Answering (VQA) v2](https://visualqa.org/challenge.html) - We translated this dataset to the four languages using `Helsinki-NLP` Marian models. The translated data can be found at [flax-community/multilingual-vqa](https://huggingface.co/datasets/flax-community/multilingual-vqa).
The checkpoints for the fine-tuned model on this pre-trained checkpoint can be found [here](https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tensorboard).
The fine-tuned model achieves eval accuracy of 49% on our validation dataset.
## Team Members
- Gunjan Chhablani [@gchhablani](https://hf.co/gchhablani)
- Bhavitvya Malik[@bhavitvyamalik](https://hf.co/bhavitvyamalik)
## Acknowledgements
We thank [Nilakshan Kunananthaseelan](https://huggingface.co/knilakshan20) for helping us whenever he could get a chance. We also thank [Abheesht Sharma](https://huggingface.co/abheesht) for helping in the discussions in the initial phases. [Luke Melas](https://github.com/lukemelas) helped us get the CC-12M data on our TPU-VMs and we are very grateful to him.
This project would not be possible without the help of [Patrick](https://huggingface.co/patrickvonplaten) and [Suraj](https://huggingface.co/valhalla) who met with us frequently and helped review our approach and guided us throughout the project.
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week and for answering our queries on the Slack channel, and for providing us with the TPU-VMs.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
huggingtweets/grapefried | huggingtweets | 2021-07-21T08:54:37Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/grapefried/1626857673378/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1392696284549632008/QOl3l-zh_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ju1ce💎</div>
<div style="text-align: center; font-size: 14px;">@grapefried</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ju1ce💎.
| Data | ju1ce💎 |
| --- | --- |
| Tweets downloaded | 2034 |
| Retweets | 504 |
| Short tweets | 403 |
| Tweets kept | 1127 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1actx5cl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @grapefried's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/grapefried')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/clip-vision-bert-cc12m-70k | flax-community | 2021-07-21T08:48:04Z | 7 | 1 | transformers | [
"transformers",
"jax",
"clip-vision-bert",
"fill-mask",
"arxiv:1908.03557",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # CLIP-Vision-BERT Multilingual Pre-trained Model
Pretrained CLIP-Vision-BERT pre-trained on translated [Conceptual-12M](https://github.com/google-research-datasets/conceptual-12m) image-text pairs using a masked language modeling (MLM) objective. 10M cleaned image-text pairs are translated using [mBART-50 one-to-many model](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) to 2.5M examples each in English, French, German and Spanish. This model is based on the VisualBERT which was introduced in
[this paper](https://arxiv.org/abs/1908.03557) and first released in
[this repository](https://github.com/uclanlp/visualbert). We trained CLIP-Vision-BERT model during community week hosted by Huggingface 🤗 using JAX/Flax.
This checkpoint is pre-trained for 70k steps.
## Model description
CLIP-Vision-BERT is a modified BERT model which takes in visual embeddings from CLIP-Vision transformer and concatenates them with BERT textual embeddings before passing them to the self-attention layers of BERT. This is done for deep cross-modal interaction between the two modes.
## Intended uses & limitations❗️
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks such as visuo-linguistic sequence classification or visual question answering. We used this model to fine-tuned on a multi-translated version of the visual question answering task - [VQA v2](https://visualqa.org/challenge.html). Since Conceptual-12M is a dataset scraped from the internet, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly with a pipeline for masked language modeling. You will need to clone the model from [here](https://github.com/gchhablani/multilingual-vqa). An example of usage is shown below:
```python
>>> from torchvision.io import read_image
>>> import numpy as np
>>> import os
>>> from transformers import CLIPProcessor, BertTokenizerFast
>>> from model.flax_clip_vision_bert.modeling_clip_vision_bert import FlaxCLIPVisionBertForMaskedLM
>>> image_path = os.path.join('images/val2014', os.listdir('images/val2014')[0])
>>> img = read_image(image_path)
>>> clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.
>>> clip_outputs = clip_processor(images=img)
>>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images.
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
>>> model = FlaxCLIPVisionBertForMaskedLM.from_pretrained('flax-community/clip-vision-bert-cc12m-70k')
>>> text = "Three teddy [MASK] in a showcase."
>>> tokens = tokenizer([text], return_tensors="np")
>>> pixel_values = np.concatenate([clip_outputs['pixel_values']])
>>> outputs = model(pixel_values=pixel_values, **tokens)
>>> indices = np.where(tokens['input_ids']==tokenizer.mask_token_id)
>>> preds = outputs.logits[indices][0]
>>> sorted_indices = np.argsort(preds)[::-1] # Get reverse sorted scores
>>> top_5_indices = sorted_indices[:5]
>>> top_5_tokens = tokenizer.convert_ids_to_tokens(top_5_indices)
>>> top_5_scores = preds[top_5_indices]
>>> print(dict(zip(top_5_tokens, top_5_scores)))
{'bears': 19.400345, 'bear': 17.866995, 'animals': 14.453735, 'dogs': 14.427426, 'girls': 14.097499}
```
## Training data 🏋🏻♂️
The CLIP-Vision-BERT model was pre-trained on a translated version of the Conceptual-12m dataset in four languages using mBART-50: English, French, German and Spanish, with 2.5M image-text pairs in each.
The dataset captions and image urls can be downloaded from [flax-community/conceptual-12m-mbart-50-translated](https://huggingface.co/datasets/flax-community/conceptual-12m-mbart-50-multilingual).
## Data Cleaning 🧹
Though the original dataset contains 12M image-text pairs, a lot of the URLs are invalid now, and in some cases, images are corrupt or broken. We remove such examples from our data, which leaves us with approximately 10M image-text pairs.
**Splits**
We used 99% of the 10M examples as a train set, and the remaining ~ 100K examples as our validation set.
## Training procedure 👨🏻💻
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of approximately 110,000. The beginning of a new document is marked with `[CLS]` and the end of one by `[CLS]`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The visual embeddings are taken from the CLIP-Vision model and combined with the textual embeddings inside the BERT embedding layer. The padding is done in the middle. Here is an example of what the embeddings look like:
```
[CLS Emb] [Textual Embs] [SEP Emb] [Pad Embs] [Visual Embs]
```
A total length of 128 tokens, including the visual embeddings, is used. The texts are truncated or padded accordingly.
### Pretraining
The checkpoint of the model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 70k steps with a per device batch size of 64 and a max sequence length of 128. The optimizer used is Adafactor with a learning rate of 1e-4, learning rate warmup for 1,000 steps, and linear decay of the learning rate after.
We tracked experiments using TensorBoard. Here is the link to the main dashboard: [CLIP Vision BERT CC12M Pre-training Dashboard](https://huggingface.co/flax-community/multilingual-vqa-pt-ckpts/tensorboard)
#### **Pretraining Results 📊**
The model at this checkpoint reached **eval accuracy of 67.85%** and **with train loss at 1.756 and eval loss at 1.706**.
## Team Members
- Gunjan Chhablani [@gchhablani](https://hf.co/gchhablani)
- Bhavitvya Malik[@bhavitvyamalik](https://hf.co/bhavitvyamalik)
## Acknowledgements
We thank [Nilakshan Kunananthaseelan](https://huggingface.co/knilakshan20) for helping us whenever he could get a chance. We also thank [Abheesht Sharma](https://huggingface.co/abheesht) for helping in the discussions in the initial phases. [Luke Melas](https://github.com/lukemelas) helped us get the CC-12M data on our TPU-VMs and we are very grateful to him.
This project would not be possible without the help of [Patrick](https://huggingface.co/patrickvonplaten) and [Suraj](https://huggingface.co/valhalla) who met with us frequently and helped review our approach and guided us throughout the project.
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week and for answering our queries on the Slack channel, and for providing us with the TPU-VMs.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
junnyu/uer_large | junnyu | 2021-07-21T08:42:35Z | 4 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: zh
tags:
- bert
- pytorch
widget:
- text: "巴黎是[MASK]国的首都。"
---
https://github.com/dbiir/UER-py/wiki/Modelzoo 中的
MixedCorpus+BertEncoder(large)+MlmTarget
https://share.weiyun.com/5G90sMJ
Pre-trained on mixed large Chinese corpus. The configuration file is bert_large_config.json
## 引用
```tex
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}
```
|
lg/ghpy_20k | lg | 2021-07-20T23:55:56Z | 10 | 2 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | **This model is provided with no guarantees whatsoever; use at your own risk.**
This is a Neo2.7B model fine tuned on github data scraped by an EleutherAI member (filtered for python-only) for 20k steps. A better code model is coming soon™ (hopefully, maybe); this model was created mostly as a test of infrastructure code. |
ifis-zork/IFIS_ZORK_AI_MEDIUM_HORROR | ifis-zork | 2021-07-20T23:14:58Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: IFIS_ZORK_AI_MEDIUM_HORROR
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IFIS_ZORK_AI_MEDIUM_HORROR
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
espnet/kan-bayashi_csmsc_conformer_fastspeech2 | espnet | 2021-07-20T21:31:29Z | 8 | 1 | espnet | [
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | text-to-speech | 2022-03-02T23:29:05Z | ---
tags:
- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/csmsc_conformer_fastspeech2`
♻️ Imported from https://zenodo.org/record/4031955/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
ifis-zork/ZORK_AI_FAN_TEMP | ifis-zork | 2021-07-20T19:58:38Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_FAN_TEMP
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_FAN_TEMP
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
ifis-zork/ZORK_AI_MODERN_A | ifis-zork | 2021-07-20T19:37:56Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_MODERN_A
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK_AI_MODERN_A
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
huggingtweets/sharsenko | huggingtweets | 2021-07-20T16:08:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/sharsenko/1626797315466/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1411529618180431873/Eyc2bjZV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Willo</div>
<div style="text-align: center; font-size: 14px;">@sharsenko</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Willo.
| Data | Willo |
| --- | --- |
| Tweets downloaded | 1279 |
| Retweets | 304 |
| Short tweets | 219 |
| Tweets kept | 756 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1r0bziin/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sharsenko's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37iziw4p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37iziw4p/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sharsenko')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
idrimadrid/autonlp-creator_classifications-4021083 | idrimadrid | 2021-07-20T12:57:16Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:idrimadrid/autonlp-data-creator_classifications",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- idrimadrid/autonlp-data-creator_classifications
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 4021083
## Validation Metrics
- Loss: 0.6848716735839844
- Accuracy: 0.8825910931174089
- Macro F1: 0.41301646762109634
- Micro F1: 0.8825910931174088
- Weighted F1: 0.863740586166105
- Macro Precision: 0.4129337301330573
- Micro Precision: 0.8825910931174089
- Weighted Precision: 0.8531335941587811
- Macro Recall: 0.44466614072309585
- Micro Recall: 0.8825910931174089
- Weighted Recall: 0.8825910931174089
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/idrimadrid/autonlp-creator_classifications-4021083
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("idrimadrid/autonlp-creator_classifications-4021083", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("idrimadrid/autonlp-creator_classifications-4021083", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
flax-community/roberta-hindi | flax-community | 2021-07-20T12:50:29Z | 105 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
widget:
- text: "मुझे उनसे बात करना <mask> अच्छा लगा"
- text: "हम आपके सुखद <mask> की कामना करते हैं"
- text: "सभी अच्छी चीजों का एक <mask> होता है"
---
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison demo is available here](https://huggingface.co/spaces/flax-community/roberta-hindi).
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-roberta-from-scratch-in-hindi/7091), organized by [Hugging Face](https://huggingface.co/) and TPU usage sponsored by Google.
## Model description
RoBERTa Hindi is a transformers model pretrained on a large corpus of Hindi data(a combination of **mc4, oscar and indic-nlp** datasets)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi')
>>> unmasker("हम आपके सुखद <mask> की कामना करते हैं")
[{'score': 0.3310680091381073,
'sequence': 'हम आपके सुखद सफर की कामना करते हैं',
'token': 1349,
'token_str': ' सफर'},
{'score': 0.15317578613758087,
'sequence': 'हम आपके सुखद पल की कामना करते हैं',
'token': 848,
'token_str': ' पल'},
{'score': 0.07826550304889679,
'sequence': 'हम आपके सुखद समय की कामना करते हैं',
'token': 453,
'token_str': ' समय'},
{'score': 0.06304813921451569,
'sequence': 'हम आपके सुखद पहल की कामना करते हैं',
'token': 404,
'token_str': ' पहल'},
{'score': 0.058322224766016006,
'sequence': 'हम आपके सुखद अवसर की कामना करते हैं',
'token': 857,
'token_str': ' अवसर'}]
```
## Training data
The RoBERTa Hindi model was pretrained on the reunion of the following datasets:
- [OSCAR](https://huggingface.co/datasets/oscar) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
- [mC4](https://huggingface.co/datasets/mc4) is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
- [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) is a natural language understanding benchmark.
- [Samanantar](https://indicnlp.ai4bharat.org/samanantar/) is a parallel corpora collection for Indic language.
- [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-and-large-summarization-corpus) is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
- [Hindi Text Short Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-summarization-corpus) is a collection of ~330k articles with their headlines collected from Hindi News Websites.
- [Old Newspapers Hindi](https://www.kaggle.com/crazydiv/oldnewspapershindi) is a cleaned subset of HC Corpora newspapers.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`.
- We had to perform cleanup of **mC4** and **oscar** datasets by removing all non hindi (non Devanagari) characters from the datasets.
- We tried to filter out evaluation set of WikiNER of [IndicGlue](https://indicnlp.ai4bharat.org/indic-glue/) benchmark by [manual labelling](https://github.com/amankhandelia/roberta_hindi/blob/master/wikiner_incorrect_eval_set.csv) where the actual labels were not correct and modifying the [downstream evaluation dataset](https://github.com/amankhandelia/roberta_hindi/blob/master/utils.py).
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores).A randomized shuffle of combined dataset of **mC4, oscar** and other datasets listed above was used to train the model. Training logs are present in [wandb](https://wandb.ai/wandb/hf-flax-roberta-hindi).
## Evaluation Results
RoBERTa Hindi is evaluated on various downstream tasks. The results are summarized below.
| Task | Task Type | IndicBERT | HindiBERTa | Indic Transformers Hindi BERT | RoBERTa Hindi Guj San | RoBERTa Hindi |
|-------------------------|----------------------|-----------|------------|-------------------------------|-----------------------|---------------|
| BBC News Classification | Genre Classification | **76.44** | 66.86 | **77.6** | 64.9 | 73.67 |
| WikiNER | Token Classification | - | 90.68 | **95.09** | 89.61 | **92.76** |
| IITP Product Reviews | Sentiment Analysis | **78.01** | 73.23 | **78.39** | 66.16 | 75.53 |
| IITP Movie Reviews | Sentiment Analysis | 60.97 | 52.26 | **70.65** | 49.35 | **61.29** |
## Team Members
- Aman K ([amankhandelia](https://huggingface.co/amankhandelia))
- Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk))
- Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv))
- Prateek Agrawal ([prateekagrawal](https://huggingface.co/prateekagrawal))
- Rahul Dev ([mlkorra](https://huggingface.co/mlkorra))
## Credits
Huge thanks to Hugging Face 🤗 & Google Jax/Flax team for such a wonderful community week, especially for providing such massive computing resources. Big thanks to [Suraj Patil](https://huggingface.co/valhalla) & [Patrick von Platen](https://huggingface.co/patrickvonplaten) for mentoring during the whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:medium> |
Amrrs/indian-foods | Amrrs | 2021-07-20T10:20:55Z | 100 | 4 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:04Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: indian-foods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9285714030265808
---
# indian-foods
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### idli
#### kachori
#### pani puri
#### samosa
#### vada pav
 |
jiaqianjing/chinese-address-ner | jiaqianjing | 2021-07-20T08:59:34Z | 15 | 5 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: chinese-address-ner
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.975825946817083
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-address-ner
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1080
- Precision: 0.9664
- Recall: 0.9774
- F1: 0.9719
- Accuracy: 0.9758
## Model description
输入一串地址中文信息，比如快递单：`北京市海淀区西北旺东路10号院(马连洼街道西北旺社区东北方向)`，按照行政级别（总有 7 级）抽取地址信息，返回每个 token 的类别。具体类别含义表示如下：
| 返回类别 | BIO 体系 | 解释 |
| ----------- | -------- | ---------------------- |
| **LABEL_0** | O | 忽略信息 |
| **LABEL_1** | B-A1 | 第一级地址（头） |
| **LABEL_2** | I-A1 | 第一级地址（其余部分） |
| ... | ... | ... |
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.5055 | 1.0 | 7 | 1.6719 | 0.1977 | 0.2604 | 0.2248 | 0.5649 |
| 1.837 | 2.0 | 14 | 1.0719 | 0.4676 | 0.6 | 0.5256 | 0.7421 |
| 1.0661 | 3.0 | 21 | 0.7306 | 0.6266 | 0.7472 | 0.6816 | 0.8106 |
| 0.8373 | 4.0 | 28 | 0.5197 | 0.6456 | 0.8113 | 0.7191 | 0.8614 |
| 0.522 | 5.0 | 35 | 0.3830 | 0.7667 | 0.8679 | 0.8142 | 0.9001 |
| 0.4295 | 6.0 | 42 | 0.3104 | 0.8138 | 0.8906 | 0.8505 | 0.9178 |
| 0.3483 | 7.0 | 49 | 0.2453 | 0.8462 | 0.9132 | 0.8784 | 0.9404 |
| 0.2471 | 8.0 | 56 | 0.2081 | 0.8403 | 0.9132 | 0.8752 | 0.9428 |
| 0.2299 | 9.0 | 63 | 0.1979 | 0.8419 | 0.9245 | 0.8813 | 0.9420 |
| 0.1761 | 10.0 | 70 | 0.1823 | 0.8830 | 0.9396 | 0.9104 | 0.9500 |
| 0.1434 | 11.0 | 77 | 0.1480 | 0.9036 | 0.9547 | 0.9284 | 0.9629 |
| 0.134 | 12.0 | 84 | 0.1341 | 0.9173 | 0.9623 | 0.9392 | 0.9678 |
| 0.128 | 13.0 | 91 | 0.1365 | 0.9375 | 0.9623 | 0.9497 | 0.9694 |
| 0.0824 | 14.0 | 98 | 0.1159 | 0.9557 | 0.9774 | 0.9664 | 0.9734 |
| 0.0744 | 15.0 | 105 | 0.1092 | 0.9591 | 0.9736 | 0.9663 | 0.9766 |
| 0.0569 | 16.0 | 112 | 0.1117 | 0.9556 | 0.9736 | 0.9645 | 0.9742 |
| 0.0559 | 17.0 | 119 | 0.1040 | 0.9628 | 0.9774 | 0.9700 | 0.9790 |
| 0.0456 | 18.0 | 126 | 0.1052 | 0.9593 | 0.9774 | 0.9682 | 0.9782 |
| 0.0405 | 19.0 | 133 | 0.1133 | 0.9590 | 0.9698 | 0.9644 | 0.9718 |
| 0.0315 | 20.0 | 140 | 0.1060 | 0.9591 | 0.9736 | 0.9663 | 0.9750 |
| 0.0262 | 21.0 | 147 | 0.1087 | 0.9554 | 0.9698 | 0.9625 | 0.9718 |
| 0.0338 | 22.0 | 154 | 0.1183 | 0.9625 | 0.9698 | 0.9662 | 0.9726 |
| 0.0225 | 23.0 | 161 | 0.1080 | 0.9664 | 0.9774 | 0.9719 | 0.9758 |
| 0.028 | 24.0 | 168 | 0.1057 | 0.9591 | 0.9736 | 0.9663 | 0.9742 |
| 0.0202 | 25.0 | 175 | 0.1062 | 0.9628 | 0.9774 | 0.9700 | 0.9766 |
| 0.0168 | 26.0 | 182 | 0.1097 | 0.9664 | 0.9774 | 0.9719 | 0.9758 |
| 0.0173 | 27.0 | 189 | 0.1093 | 0.9628 | 0.9774 | 0.9700 | 0.9774 |
| 0.0151 | 28.0 | 196 | 0.1162 | 0.9628 | 0.9774 | 0.9700 | 0.9766 |
| 0.0135 | 29.0 | 203 | 0.1126 | 0.9483 | 0.9698 | 0.9590 | 0.9758 |
| 0.0179 | 30.0 | 210 | 0.1100 | 0.9449 | 0.9698 | 0.9572 | 0.9774 |
| 0.0161 | 31.0 | 217 | 0.1098 | 0.9449 | 0.9698 | 0.9572 | 0.9766 |
| 0.0158 | 32.0 | 224 | 0.1191 | 0.9483 | 0.9698 | 0.9590 | 0.9734 |
| 0.0151 | 33.0 | 231 | 0.1058 | 0.9483 | 0.9698 | 0.9590 | 0.9750 |
| 0.0121 | 34.0 | 238 | 0.0990 | 0.9593 | 0.9774 | 0.9682 | 0.9790 |
| 0.0092 | 35.0 | 245 | 0.1128 | 0.9519 | 0.9698 | 0.9607 | 0.9774 |
| 0.0097 | 36.0 | 252 | 0.1181 | 0.9627 | 0.9736 | 0.9681 | 0.9766 |
| 0.0118 | 37.0 | 259 | 0.1185 | 0.9591 | 0.9736 | 0.9663 | 0.9782 |
| 0.0118 | 38.0 | 266 | 0.1021 | 0.9557 | 0.9774 | 0.9664 | 0.9823 |
| 0.0099 | 39.0 | 273 | 0.1000 | 0.9559 | 0.9811 | 0.9683 | 0.9815 |
| 0.0102 | 40.0 | 280 | 0.1025 | 0.9559 | 0.9811 | 0.9683 | 0.9815 |
| 0.0068 | 41.0 | 287 | 0.1080 | 0.9522 | 0.9774 | 0.9646 | 0.9807 |
| 0.0105 | 42.0 | 294 | 0.1157 | 0.9449 | 0.9698 | 0.9572 | 0.9766 |
| 0.0083 | 43.0 | 301 | 0.1207 | 0.9380 | 0.9698 | 0.9536 | 0.9766 |
| 0.0077 | 44.0 | 308 | 0.1208 | 0.9483 | 0.9698 | 0.9590 | 0.9766 |
| 0.0077 | 45.0 | 315 | 0.1176 | 0.9483 | 0.9698 | 0.9590 | 0.9774 |
| 0.0071 | 46.0 | 322 | 0.1137 | 0.9483 | 0.9698 | 0.9590 | 0.9790 |
| 0.0075 | 47.0 | 329 | 0.1144 | 0.9483 | 0.9698 | 0.9590 | 0.9782 |
| 0.0084 | 48.0 | 336 | 0.1198 | 0.9483 | 0.9698 | 0.9590 | 0.9766 |
| 0.0103 | 49.0 | 343 | 0.1217 | 0.9519 | 0.9698 | 0.9607 | 0.9766 |
| 0.0087 | 50.0 | 350 | 0.1230 | 0.9519 | 0.9698 | 0.9607 | 0.9766 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.0
- Datasets 1.9.0
- Tokenizers 0.10.3
|
huggingtweets/yigitckahyaoglu | huggingtweets | 2021-07-20T03:00:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/yigitckahyaoglu/1626750011426/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1407084026507182089/ywRe7M0Z_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">yiğit</div>
<div style="text-align: center; font-size: 14px;">@yigitckahyaoglu</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from yiğit.
| Data | yiğit |
| --- | --- |
| Tweets downloaded | 1671 |
| Retweets | 165 |
| Short tweets | 64 |
| Tweets kept | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cqhj21l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yigitckahyaoglu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yigitckahyaoglu')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
mnaylor/base-bert-finetuned-mtsamples | mnaylor | 2021-07-19T15:53:35Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # BERT Base Fine-tuned on MTSamples
This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
|
flax-community/mr-indicnlp-classifier | flax-community | 2021-07-19T12:53:33Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | # IndicNLP Marathi News Classifier
This model was fine-tuned using [Marathi RoBERTa](https://huggingface.co/flax-community/roberta-base-mr) on [IndicNLP Marathi News Dataset](https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset)
## Dataset
IndicNLP Marathi news dataset consists 3 classes - `['lifestyle', 'entertainment', 'sports']` - with following docs distribution as per classes:
| train | eval | test |
| ----- | ---- | ---- |
| 9672 | 477 | 478 |
💯 Our **`mr-indicnlp-classifier`** model fine tuned from **roberta-base-mr** Pretrained Marathi RoBERTa model outperformed both classifier mentioned in [Arora, G. (2020). iNLTK](https://www.semanticscholar.org/paper/iNLTK%3A-Natural-Language-Toolkit-for-Indic-Languages-Arora/5039ed9e100d3a1cbbc25a02c82f6ee181609e83/figure/3) and [Kunchukuttan, Anoop et al. AI4Bharat-IndicNLP.](https://www.semanticscholar.org/paper/AI4Bharat-IndicNLP-Corpus%3A-Monolingual-Corpora-and-Kunchukuttan-Kakwani/7997d432925aff0ba05497d2893c09918298ca55/figure/4)
| Dataset | FT-W | FT-WC | INLP | iNLTK | **roberta-base-mr 🏆** |
| --------------- | ----- | ----- | ----- | ----- | --------------------- |
| iNLTK Headlines | 83.06 | 81.65 | 89.92 | 92.4 | **97.48** |
|
flax-community/wav2vec2-dhivehi | flax-community | 2021-07-19T09:40:30Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"wav2vec2",
"pretraining",
"automatic-speech-recognition",
"dv",
"dataset:common_voice",
"arxiv:2006.11477",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: dv
tags:
- automatic-speech-recognition
datasets:
- common_voice
---
# Wav2Vec2 Dhivehi
Wav2vec2 pre-pretrained from scratch using common voice dhivehi dataset. The model was trained with Flax during the [Flax/Jax Community Week](https://discss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organised by HuggingFace.
## Model description
The model used for training is [Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by FacebookAI. It was introduced in the paper
"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations" by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli (https://arxiv.org/abs/2006.11477).
This model is available in the 🤗 [Model Hub](https://huggingface.co/facebook/wav2vec2-base-960h).
## Training data
Dhivehi data from [Common Voice](https://commonvoice.mozilla.org/en/datasets).
The dataset is also available in the 🤗 [Datasets](https://huggingface.co/datasets/common_voice) library.
## Team members
- Shahu Kareem ([@shahukareem](https://huggingface.co/shahukareem))
- Eyna ([@eyna](https://huggingface.co/eyna))
|
huggingtweets/deathbattlebot | huggingtweets | 2021-07-19T06:42:58Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/deathbattlebot/1626676974616/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1295317265257238529/8q3IptgS_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Death Battle Bot</div>
<div style="text-align: center; font-size: 14px;">@deathbattlebot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Death Battle Bot.
| Data | Death Battle Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1 |
| Short tweets | 20 |
| Tweets kept | 3229 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hcf8oqg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deathbattlebot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2d0vuhj5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2d0vuhj5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/deathbattlebot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/wav2vec2-spanish | flax-community | 2021-07-19T05:02:39Z | 4 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"es",
"dataset:common_voice",
"arxiv:2006.11477",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: es
tags:
- audio
- automatic-speech-recognition
datasets:
- common_voice
---
# Wav2Vec2 Spanish
Wav2Vec2 model pre-trained using the Spanish portion of the Common Voice dataset. The model is trained with Flax and using TPUs sponsored by Google since this is part of the [Flax/Jax Community Week](https://discss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organised by HuggingFace.
## Model description
The model used for training is [Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by FacebookAI. It was introduced in the paper
"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations" by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli (https://arxiv.org/abs/2006.11477).
This model is available in the 🤗 [Model Hub](https://huggingface.co/facebook/wav2vec2-base-960h).
## Training data
Spanish portion of [Common Voice](https://commonvoice.mozilla.org/en/datasets). Common Voice is an open source, multi-language dataset of voices part of Mozilla's initiative to help teach machines how real people speak.
The dataset is also available in the 🤗 [Datasets](https://huggingface.co/datasets/common_voice) library.
## Team members
- María Grandury ([@mariagrandury](https://github.com/mariagrandury))
- Manuel Romero ([@mrm8488](https://huggingface.co/mrm8488))
- Eduardo González Ponferrada ([@edugp](https://huggingface.co/edugp))
- pcuenq ([@pcuenq](https://huggingface.co/pcuenq)) |
huggingtweets/heyarav | huggingtweets | 2021-07-19T01:28:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1416877970132672512/942NnDJA_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Arav</div>
<div style="text-align: center; font-size: 14px;">@heyarav</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Arav.
| Data | Arav |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 411 |
| Short tweets | 786 |
| Tweets kept | 2049 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n441q7z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @heyarav's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s8u4vm6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s8u4vm6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/heyarav')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
andi611/distilbert-base-uncased-qa-with-ner | andi611 | 2021-07-19T01:20:54Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-qa-with-ner
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-qa-with-ner
This model is a fine-tuned version of [andi611/distilbert-base-uncased-qa](https://huggingface.co/andi611/distilbert-base-uncased-qa) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
huggingtweets/ellis_hughes | huggingtweets | 2021-07-18T18:42:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/ellis_hughes/1626633732954/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1004536007012651008/ZWJUeJ2W_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ellis Hughes</div>
<div style="text-align: center; font-size: 14px;">@ellis_hughes</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ellis Hughes.
| Data | Ellis Hughes |
| --- | --- |
| Tweets downloaded | 2170 |
| Retweets | 396 |
| Short tweets | 91 |
| Tweets kept | 1683 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rqrdlum/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ellis_hughes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n17xu9k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n17xu9k/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ellis_hughes')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
clip-italian/clip-italian-final | clip-italian | 2021-07-18T16:35:01Z | 12 | 0 | transformers | [
"transformers",
"jax",
"hybrid-clip",
"italian",
"bert",
"vit",
"vision",
"it",
"dataset:wit",
"dataset:ctl/conceptualCaptions",
"dataset:mscoco-it",
"arxiv:2103.00020",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language: it
license:
datasets:
- wit
- ctl/conceptualCaptions
- mscoco-it
tags:
- italian
- bert
- vit
- vision
---
# CLIP-Italian
CLIP Italian is a CLIP-like Model for Italian. The CLIP model (Contrastive Language–Image Pre-training) was developed by researchers at OpenAI and is able to efficiently learn visual concepts from natural language supervision.
We fine-tuned a competitive Italian CLIP model with only ~1.4 million Italian image-text pairs. This model is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Training Data
We considered three main sources of data:
- [WIT](https://github.com/google-research-datasets/wit)
- [MSCOCO-IT](https://github.com/crux82/mscoco-it)
- [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/)
## Training Procedure
Preprocessing, hardware used, hyperparameters...
## Evaluation Performance
## Limitations
## Usage
## Team members
- Federico Bianchi ([vinid](https://huggingface.co/vinid))
- Raphael Pisoni ([4rtemi5](https://huggingface.co/4rtemi5))
- Giuseppe Attanasio ([g8a9](https://huggingface.co/g8a9))
- Silvia Terragni ([silviatti](https://huggingface.co/silviatti))
- Dario Balestri ([D3Reo](https://huggingface.co/D3Reo))
- Gabriele Sarti ([gsarti](https://huggingface.co/gsarti))
- Sri Lakshmi ([srisweet](https://huggingface.co/srisweet))
## Useful links
- [CLIP Blog post](https://openai.com/blog/clip/)
- [CLIP paper](https://arxiv.org/abs/2103.00020)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Community Week channel](https://discord.com/channels/858019234139602994/859711887520038933)
- [Hybrid CLIP example scripts](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects/hybrid_clip)
- [Model Repository](https://huggingface.co/clip-italian/clip-italian-final/) |
flax-community/gpt-2-tamil | flax-community | 2021-07-18T16:03:33Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"ta",
"dataset:oscar",
"dataset:IndicNLP",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: ta
datasets:
- oscar
- IndicNLP
widget:
- text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'
---
# GPT2-Tamil
This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
## Setup:
To setup the project, run the following command,
```python
pip install -r requirements.txt
```
## Model:
Pretrained model on Tamil language using a causal language modeling (CLM) objective.
## Dataset Used:
The GTP-2 model is trained on [oscar dataset - ta](https://huggingface.co/datasets/oscar)
## Intended uses & limitations:
You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt) to look for fine-tuned versions on a task that interests you.
## How to pretrain the model:
To perform training, do the following steps,
- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
```python
>>> export MODEL_DIR=<model_dir>
```
- Create the config.json by running the following command,
```python
>>> python src/create_config.py
```
- Create the tokenizer by running the following command,
```python
>>> python src/train_tokenizer.py
```
- Once the config and tokenizer is created, run the following script to start training the flax model
```python
>>> python scripts/train_gpt2-oscar-tamil.sh
```
## How to use:
To perform language generation using the model, pipeline can be used directly.
- First convert the flax model to pytorch using the following command,
```python
python src/convert_flax_to_pytorch.py
```
- Use the following snippet to perform language generation,
```python
>>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
>>> model_name = 'abinayam/gpt-2-tamil'
>>> model = AutoModelWithLMHead.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> set_seed(42)
>>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
>>> max_len = 300
>>> no_seq = 5
>>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
>>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
```
|
sehandev/koelectra-qa | sehandev | 2021-07-18T14:21:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: koelectra-qa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# koelectra-qa
This model was trained from scratch on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
|
sehandev/koelectra-long-qa | sehandev | 2021-07-18T06:01:25Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: koelectra-long-qa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# koelectra-long-qa
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
|
huggingtweets/percyvader | huggingtweets | 2021-07-17T22:54:48Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/percyvader/1626562484510/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/848218865528078336/OTr3Lo3N_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">trades cowboy hat for fedora</div>
<div style="text-align: center; font-size: 14px;">@percyvader</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from trades cowboy hat for fedora.
| Data | trades cowboy hat for fedora |
| --- | --- |
| Tweets downloaded | 2818 |
| Retweets | 628 |
| Short tweets | 746 |
| Tweets kept | 1444 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2vmsj6nk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @percyvader's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1euqbqf4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1euqbqf4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/percyvader')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/roberta-base-mr | flax-community | 2021-07-17T15:30:40Z | 28 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
widget:
- text: "अध्यक्ष <mask> पवार आणि उपमुख्यमंत्री अजित पवार यांची भेट घेतली."
- text: "मोठी बातमी! उद्या दुपारी <mask> वाजता जाहीर होणार दहावीचा निकाल"
---
# RoBERTa base model for Marathi language (मराठी भाषा)
Pretrained model on Marathi language using a masked language modeling (MLM) objective. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). We trained RoBERTa model for Marathi Language during community week hosted by Huggingface 🤗 using JAX/Flax for NLP & CV jax.
<img src="https://user-images.githubusercontent.com/15062408/126040902-ea8808db-ec30-4a3f-bf95-5d3b10d674e9.png" alt="huggingface-marathi-roberta" width="350" height="350" style="text-align: center">
## Model description
Marathi RoBERTa is a transformers model pretrained on a large corpus of Marathi data in a self-supervised fashion.
## Intended uses & limitations❗️
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. We used this model to fine tune on text classification task for iNLTK and indicNLP news text classification problem statement. Since marathi mc4 dataset is made by scraping marathi newspapers text, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-base-mr')
>>> unmasker("मोठी बातमी! उद्या दुपारी <mask> वाजता जाहीर होणार दहावीचा निकाल")
[{'score': 0.057209037244319916,'sequence': 'मोठी बातमी! उद्या दुपारी आठ वाजता जाहीर होणार दहावीचा निकाल',
'token': 2226,
'token_str': 'आठ'},
{'score': 0.02796074189245701,
'sequence': 'मोठी बातमी! उद्या दुपारी २० वाजता जाहीर होणार दहावीचा निकाल',
'token': 987,
'token_str': '२०'},
{'score': 0.017235398292541504,
'sequence': 'मोठी बातमी! उद्या दुपारी नऊ वाजता जाहीर होणार दहावीचा निकाल',
'token': 4080,
'token_str': 'नऊ'},
{'score': 0.01691395975649357,
'sequence': 'मोठी बातमी! उद्या दुपारी २१ वाजता जाहीर होणार दहावीचा निकाल',
'token': 1944,
'token_str': '२१'},
{'score': 0.016252165660262108,
'sequence': 'मोठी बातमी! उद्या दुपारी ३ वाजता जाहीर होणार दहावीचा निकाल',
'token': 549,
'token_str': ' ३'}]
```
## Training data 🏋🏻♂️
The RoBERTa Marathi model was pretrained on `mr` dataset of C4 multilingual dataset:
<br>
<br>
[C4 (Colossal Clean Crawled Corpus)](https://yknzhu.wixsite.com/mbweb), Introduced by Raffel et al. in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://paperswithcode.com/paper/exploring-the-limits-of-transfer-learning).
The dataset can be downloaded in a pre-processed form from [allennlp](https://github.com/allenai/allennlp/discussions/5056) or huggingface's datsets - [mc4 dataset](https://huggingface.co/datasets/mc4).
Marathi (`mr`) dataset consists of 14 billion tokens, 7.8 million docs and with weight ~70 GB of text.
## Data Cleaning 🧹
Though initial `mc4` marathi corpus size ~70 GB, Through data exploration, it was observed it contains docs from different languages especially thai, chinese etc. So we had to clean the dataset before traning tokenizer and model. Surprisingly, results after cleaning Marathi mc4 corpus data:
#### **Train set:**
Clean docs count 1581396 out of 7774331. <br>
**~20.34%** of whole marathi train split is actually Marathi.
#### **Validation set**
Clean docs count 1700 out of 7928. <br>
**~19.90%** of whole marathi validation split is actually Marathi.
## Training procedure 👨🏻💻
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 42K steps with a batch size of 128 and a sequence length of 128. The
optimizer used is Adam with a learning rate of 3e-4, β1 = 0.9, β2 = 0.98 and
ε = 1e-8, a weight decay of 0.01, learning rate warmup for 1,000 steps and linear decay of the learning
rate after.
We tracked experiments and hyperparameter tunning on weights and biases platform. Here is link to main dashboard: <br>
[Link to Weights and Biases Dashboard for Marathi RoBERTa model](https://wandb.ai/nipunsadvilkar/roberta-base-mr/runs/19qtskbg?workspace=user-nipunsadvilkar)
#### **Pretraining Results 📊**
RoBERTa Model reached **eval accuracy of 85.28%** around ~35K step **with train loss at 0.6507 and eval loss at 0.6219**.
## Fine Tuning on downstream tasks
We performed fine-tuning on downstream tasks. We used following datasets for classification:
1. [IndicNLP Marathi news classification](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets)
2. [iNLTK Marathi news headline classification](https://www.kaggle.com/disisbig/marathi-news-dataset)
### **Fine tuning on downstream task results (Segregated)**
#### 1. [IndicNLP Marathi news classification](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets)
IndicNLP Marathi news dataset consists 3 classes - `['lifestyle', 'entertainment', 'sports']` - with following docs distribution as per classes:
| train | eval | test
| -- | -- | --
| 9672 | 477 | 478
💯 Our Marathi RoBERTa **`roberta-base-mr` model outperformed both classifier ** mentioned in [Arora, G. (2020). iNLTK](https://www.semanticscholar.org/paper/iNLTK%3A-Natural-Language-Toolkit-for-Indic-Languages-Arora/5039ed9e100d3a1cbbc25a02c82f6ee181609e83/figure/3) and [Kunchukuttan, Anoop et al. AI4Bharat-IndicNLP.](https://www.semanticscholar.org/paper/AI4Bharat-IndicNLP-Corpus%3A-Monolingual-Corpora-and-Kunchukuttan-Kakwani/7997d432925aff0ba05497d2893c09918298ca55/figure/4)
Dataset | FT-W | FT-WC | INLP | iNLTK | **roberta-base-mr 🏆**
-- | -- | -- | -- | -- | --
iNLTK Headlines | 83.06 | 81.65 | 89.92 | 92.4 | **97.48**
**🤗 Huggingface Model hub repo:**<br>
`roberta-base-mr` fine tuned on iNLTK Headlines classification dataset model:
[**`flax-community/mr-indicnlp-classifier`**](https://huggingface.co/flax-community/mr-indicnlp-classifier)
🧪 Fine tuning experiment's weight and biases dashboard [link](https://wandb.ai/nipunsadvilkar/huggingface/runs/1242bike?workspace=user-nipunsadvilkar
)
#### 2. [iNLTK Marathi news headline classification](https://www.kaggle.com/disisbig/marathi-news-dataset)
This dataset consists 3 classes - `['state', 'entertainment', 'sports']` - with following docs distribution as per classes:
| train | eval | test
| -- | -- | --
| 9658 | 1210 | 1210
💯 Here as well **`roberta-base-mr` outperformed `iNLTK` marathi news text classifier**.
Dataset | iNLTK ULMFiT | **roberta-base-mr 🏆**
-- | -- | --
iNLTK news dataset (kaggle) | 92.4 | **94.21**
**🤗 Huggingface Model hub repo:**<br>
`roberta-base-mr` fine tuned on iNLTK news classification dataset model:
[**`flax-community/mr-inltk-classifier`**](https://huggingface.co/flax-community/mr-inltk-classifier)
Fine tuning experiment's weight and biases dashboard [link](https://wandb.ai/nipunsadvilkar/huggingface/runs/2u5l9hon?workspace=user-nipunsadvilkar
)
## **Want to check how above models generalise on real world Marathi data?**
Head to 🤗 Huggingface's spaces 🪐 to play with all three models:
1. Mask Language Modelling with Pretrained Marathi RoBERTa model: <br>
[**`flax-community/roberta-base-mr`**](https://huggingface.co/flax-community/roberta-base-mr)
2. Marathi Headline classifier: <br>
[**`flax-community/mr-indicnlp-classifier`**](https://huggingface.co/flax-community/mr-indicnlp-classifier)
3. Marathi news classifier: <br>
[**`flax-community/mr-inltk-classifier`**](https://huggingface.co/flax-community/mr-inltk-classifier)
[Streamlit app of Pretrained Roberta Marathi model on Huggingface Spaces](https://huggingface.co/spaces/flax-community/roberta-base-mr)
## Team Members
- Nipun Sadvilkar [@nipunsadvilkar](https://github.com/nipunsadvilkar)
- Haswanth Aekula [@hassiahk](https://github.com/hassiahk)
## Credits
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week. Especially for providing such massive computing resource. Big thanks to [@patil-suraj](https://github.com/patil-suraj) & [@patrickvonplaten](https://github.com/patrickvonplaten) for mentoring during whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
flax-community/gpt2-base-thai | flax-community | 2021-07-17T10:11:12Z | 2,447 | 10 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"gpt2-base-thai",
"th",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: th
tags:
- gpt2-base-thai
license: mit
datasets:
- oscar
widget:
- text: "สวัสดีตอนเช้า"
---
## GPT-2 Base Thai
GPT-2 Base Thai is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. It was trained on the [OSCAR](https://huggingface.co/datasets/oscar) dataset, specifically the `unshuffled_deduplicated_th` subset. The model was trained from scratch and achieved an evaluation loss of 1.708 and an evaluation perplexity of 5.516.
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by HuggingFace. All training was done on a TPUv3-8 VM, sponsored by the Google Cloud team.
All necessary scripts used for training could be found in the [Files and versions](https://hf.co/flax-community/gpt2-base-thai/tree/main) tab, as well as the [Training metrics](https://hf.co/flax-community/gpt2-base-thai/tensorboard) logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ---------------- | ------- | ----- | ------------------------------------ |
| `gpt2-base-thai` | 124M | GPT-2 | `unshuffled_deduplicated_th` Dataset |
## Evaluation Results
The model was trained for 3 epochs and the following is the final result once the training ended.
| train loss | valid loss | valid PPL | total time |
| ---------- | ---------- | --------- | ---------- |
| 1.638 | 1.708 | 5.516 | 6:12:34 |
## How to Use
### As Causal Language Model
```python
from transformers import pipeline
pretrained_name = "flax-community/gpt2-base-thai"
nlp = pipeline(
"text-generation",
model=pretrained_name,
tokenizer=pretrained_name
)
nlp("สวัสดีตอนเช้า")
```
### Feature Extraction in PyTorch
```python
from transformers import GPT2Model, GPT2TokenizerFast
pretrained_name = "flax-community/gpt2-base-thai"
model = GPT2Model.from_pretrained(pretrained_name)
tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name)
prompt = "สวัสดีตอนเช้า"
encoded_input = tokenizer(prompt, return_tensors='pt')
output = model(**encoded_input)
```
## Team Members
- Sakares Saengkaew ([@sakares](https://hf.co/sakares))
- Wilson Wongso ([@w11wo](https://hf.co/w11wo))
|
birgermoell/t5-base-swedish | birgermoell | 2021-07-17T07:52:39Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"feature-extraction",
"summarization",
"translation",
"sv",
"dataset:oscar",
"arxiv:1910.10683",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | translation | 2022-03-02T23:29:05Z | ---
language:
- sv
datasets:
- oscar
tags:
- summarization
- translation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/oscar)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
|
birgermoell/swedish-gpt | birgermoell | 2021-07-17T07:45:52Z | 30 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"sv",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: sv
widget:
- text: "Jag är en svensk språkmodell."
---
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
# GPT-svenska-wikipedia
A swedish GPT2 style model trained using Flax CLM pipeline on the Swedish
part of the wiki40b dataset and the Oscar dataset.
https://huggingface.co/datasets/wiki40b
The model was trained for around 22600 steps (42 hours) as part of the Huggingface Jax/Flax challenge with the following loss and learning rate
Loss: 3.1715331077575684, Learning Rate: 0.0024816440418362617)
The model could likely be trained for longer.
## Data cleaning and preprocessing
The data was cleaned and preprocessed using the following script. Make sure to install depencies for beam_runner to make the dataset work.
```python
from datasets import load_dataset
def load_and_clean_wiki():
dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner', split="train")
#dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner')
dataset = dataset.remove_columns(['wikidata_id', 'version_id'])
filtered_dataset = dataset.map(filter_wikipedia)
# filtered_dataset[:3]
# print(filtered_dataset[:3])
return filtered_dataset
def filter_wikipedia(batch):
batch["text"] = " ".join(batch["text"].split("\
_START_SECTION_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_ARTICLE_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_ARTICLE_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_PARAGRAPH_\
"))
batch["text"] = " ".join(batch["text"].split("_NEWLINE_"))
batch["text"] = " ".join(batch["text"].split("\xa0"))
return batch
```
## Training script
The following training script was used to train the model.
```bash
./run_clm_flax.py --output_dir="${MODEL_DIR}" --model_type="gpt2" --config_name="${MODEL_DIR}" --tokenizer_name="${MODEL_DIR}" --dataset_name="wiki40b" --dataset_config_name="sv" --do_train --do_eval --block_size="512" --per_device_train_batch_size="64" --per_device_eval_batch_size="64" --learning_rate="5e-3" --warmup_steps="1000" --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" --overwrite_output_dir --num_train_epochs="20" --logging_steps="500" --save_steps="1000" --eval_steps="2500" --push_to_hub
```
|
flax-community/Sinhala-roberta | flax-community | 2021-07-17T03:27:17Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"feature-extraction",
"fill-mask",
"sinhala",
"si",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: si
tags:
- fill-mask
- sinhala
- roberta
---
## Sinhala Roberta model trained on MC4 Sinhala dataset (manually cleaned) |
huggingtweets/joshizcul | huggingtweets | 2021-07-17T01:19:01Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/joshizcul/1626484737394/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1391124699321569281/4aMGupaX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">J🔆🌒sh 💈🏵</div>
<div style="text-align: center; font-size: 14px;">@joshizcul</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from J🔆🌒sh 💈🏵.
| Data | J🔆🌒sh 💈🏵 |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 101 |
| Short tweets | 716 |
| Tweets kept | 2421 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3upndo8i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @joshizcul's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iju0kbl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iju0kbl/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/joshizcul')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/enderdev_ | huggingtweets | 2021-07-16T20:30:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/enderdev_/1626467434270/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415445991503839234/RSxcTJiJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Kieran</div>
<div style="text-align: center; font-size: 14px;">@enderdev_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Kieran.
| Data | Kieran |
| --- | --- |
| Tweets downloaded | 2518 |
| Retweets | 388 |
| Short tweets | 691 |
| Tweets kept | 1439 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2qz7ps6o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @enderdev_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3aqdw40t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3aqdw40t/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/enderdev_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
tupleblog/salim-classifier | tupleblog | 2021-07-16T20:11:16Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
widget:
- text: "รัฐรับผิดชอบทุกชีวิตไม่ได้หรอกคนให้บริการต้องจัดการเองถ้าจะเปิดผับบาร์"
---
# Salim-Classifier
**วัตถุประสงค์:** ทุกวันนี้หาเพื่อนที่รักชาติ ศาสนา พระมหากษัตริย์ รัฐบาลยากเหลือเกิน มีแต่พวกสามกีบ ควายแดงคอยจ้องจะทำร้าย
ทางทีมของเราจึงสร้างโมเดลมาเพื่อช่วยหาเพื่อนสลิ่มจากคอมเม้น ที่นับวันจะหลงเหลืออยู่น้อยยิ่งนักในสังคมไทย ทั้งนี้เพื่อเป็นแนวทางในการสร้างสังคมสลิ่มที่แข็งแรงต่อไป
## วิธีการใช้งาน
สามารถลง `transfomers` จาก Huggingface และใช้งานโมเดลได้ดังต่อไปนี้
``` py
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
pipeline
)
# download model from hub
tokenizer = AutoTokenizer.from_pretrained("tupleblog/salim-classifier")
model = AutoModelForSequenceClassification.from_pretrained("tupleblog/salim-classifier")
# using pipeline to classify an input text
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = "จิตไม่ปกติ วันๆคอยแต่ให้คนเสี้ยมทะเลาะกันด่ากัน คอยจ้องแต่จะเล่นงานรัฐบาล ความคดด้านลบ"
classifier(text)
# >> [{'label': 'HIGHLY LIKELY SALIM', 'score': 0.9989368915557861}] ยินดีด้วย น่าจะเป็นสลิ่ม!
```
## การเก็บข้อมูล
สร้างข้อมูลตัวอย่างและทำการ Annotate จากนั้นนำข้อมูลมาเทรนโมเดลด้วย WangchanBERTa
โดยข้อมูลอาจมีความ bias เนื่องจากทางทีมงานเป็นผู้เก็บข้อมูลเอง
## ทดลองใช้งานผ่าน HuggingFace
ท่านสามารถทดลองใช้งานผ่าน HuggingFace โดยใส่คอมเม้นจาก Facebook เข้าไปในช่องได้ในเว็บไซต์
[huggingface.co/tupleblog/salim-classifier](https://huggingface.co/tupleblog/salim-classifier)
**ตัวอย่างประโยค**
- รัฐรับผิดชอบทุกชีวิตไม่ได้หรอกคนให้บริการต้องจัดการเองถ้าจะเปิดผับบาร์
- แค่เคารพกฎหมาย คนพวกนี้ยังทำไม่ได้เลย แล้วจะถามหาความก้าวหน้าของประเทศ ?
- หมามันยังยืนเคารพธงชาติ แต่พวกนี้กลับทำอะไรไม่อายเดรัจฉาน
- ถ้าไม่ชอบประชาธิปไตย จะไปใช้วิธีการปกครองแบบไหนหรอครับ แล้วแบบไหนถึงดีหรอ ผมไม่เข้าใจครับอดีตผ่านไปแล้ว ทำไมไม่มองที่อนาคตกันหละครับ
- อีพวกสามกีบ`<pad>`
สำหรับข้อความที่สั้นกว่า 50 ตัวอักษรแนะนำให้เติม `<pad>` ตามหลังข้อความเพื่อความแม่นยำที่สูงขึ้น
## Performance
We report performance on 20% evaluation set (accuracy, precision, recall, F1-score macro) as follows:
| Accuracy | Precision | Recall | F1 |
| -------- | --------- | ------ | ------ |
| 86.15% | 86.12% | 86.13% | 86.13% |
|
BumBelDumBel/ZORK-AI-TEST | BumBelDumBel | 2021-07-16T17:12:42Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
model_index:
- name: ZORK-AI-TEST
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ZORK-AI-TEST
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
huggingtweets/benchestnut | huggingtweets | 2021-07-16T16:34:14Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/benchestnut/1626453250687/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1045023385816686592/7wIqU8ZY_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ben Chestnut</div>
<div style="text-align: center; font-size: 14px;">@benchestnut</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ben Chestnut.
| Data | Ben Chestnut |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 943 |
| Short tweets | 124 |
| Tweets kept | 2162 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lyrugs4q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @benchestnut's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u96gtbs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u96gtbs/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/benchestnut')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/gpt2-medium-persian | flax-community | 2021-07-16T13:01:08Z | 364 | 9 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"fa",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: fa
tags:
- text-generation
widget:
- text: "در یک اتفاق شگفت انگیز، پژوهشگران"
- text: "گرفتگی بینی در کودکان و بهخصوص نوزادان باعث میشود"
- text: "امیدواریم نوروز امسال سالی"
---
# GPT2 Medium 4 Persian
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-gpt2-from-scratch-in-persian/7560), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Team Members
- [Mehrdad Farahani](huggingface.co/m3hrdadfi)
- [Saied Alimoradi](https://discuss.huggingface.co/u/saied)
- [M. Reza Zerehpoosh](huggingface.co/ironcladgeek)
- [Hooman Sedghamiz](https://discuss.huggingface.co/u/hooman650)
- [Mazeyar Moeini Feizabadi](https://discuss.huggingface.co/u/mazy1998)
## Dataset
We used [Oscar](https://huggingface.co/datasets/oscar) dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus.
## How To Use
You can use this model directly with a pipeline for text generation.
```python
from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel
tokenizer = AutoTokenizer.from_pretrained('flax-community/gpt2-medium-persian')
model = GPT2LMHeadModel.from_pretrained('flax-community/gpt2-medium-persian')
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':100})
generated_text = generator('در یک اتفاق شگفت انگیز، پژوهشگران')
```
For using Tensorflow import TFGPT2LMHeadModel instead of GPT2LMHeadModel.
## Demo
... SOON
## Evaluation
... SOON |
flax-community/t5-v1_1-base-wikisplit | flax-community | 2021-07-16T12:40:45Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wiki_split",
"arxiv:1907.12461",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
datasets:
- wiki_split
widget:
- text: "Mary likes to play football in her freetime whenever she meets with her friends that are very nice people."
---
# T5 model for sentence splitting in English
Sentence Split is the task of dividing a long sentence into multiple sentences.
E.g.:
```
Mary likes to play football in her freetime whenever she meets with her friends that are very nice people.
```
could be split into
```
Mary likes to play football in her freetime whenever she meets with her friends.
```
```
Her friends are very nice people.
```
## How to use it in your code:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-v1_1-base-wikisplit")
model = AutoModelForSeq2SeqLM.from_pretrained("flax-community/t5-v1_1-base-wikisplit")
complex_sentence = "This comedy drama is produced by Tidy , the company she co-founded in 2008 with her husband David Peet , who is managing director ."
sample_tokenized = tokenizer(complex_sentence, return_tensors="pt")
answer = model.generate(sample_tokenized['input_ids'], attention_mask = sample_tokenized['attention_mask'], max_length=256, num_beams=5)
gene_sentence = tokenizer.decode(answer[0], skip_special_tokens=True)
gene_sentence
"""
Output:
This comedy drama is produced by Tidy. She co-founded Tidy in 2008 with her husband David Peet, who is managing director.
"""
```
## Datasets:
[Wiki_Split](https://research.google/tools/datasets/wiki-split/)
## Current Basline from [paper](https://arxiv.org/abs/1907.12461)
## Our Results:
| Model | Exact | SARI | BLEU |
| --- | --- | --- | --- |
| [t5-base-wikisplit](https://huggingface.co/flax-community/t5-base-wikisplit) | 17.93 | 67.5438 | 76.9 |
| [t5-v1_1-base-wikisplit](https://huggingface.co/flax-community/t5-v1_1-base-wikisplit) | 18.1207 | 67.4873 | 76.9478 |
| [byt5-base-wikisplit](https://huggingface.co/flax-community/byt5-base-wikisplit) | 11.3582 | 67.2685 | 73.1682 |
| [t5-large-wikisplit](https://huggingface.co/flax-community/t5-large-wikisplit) | 18.6632 | 68.0501 | 77.1881 | |
johnpaulbin/gpt2-skript-80 | johnpaulbin | 2021-07-16T05:43:37Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | GPT-2 for the Minecraft Plugin: Skript (80,000 Lines, 3< GB: GPT-2 Large model finetune)
Inferencing Colab: https://colab.research.google.com/drive/1uTAPLa1tuNXFpG0qVLSseMro6iU9-xNc |
liam168/qa-roberta-base-chinese-extractive | liam168 | 2021-07-16T05:01:19Z | 47 | 9 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: zh
widget:
- text: "著名诗歌《假如生活欺骗了你》的作者是"
context: "普希金从那里学习人民的语言，吸取了许多有益的养料，这一切对普希金后来的创作产生了很大的影响。这两年里，普希金创作了不少优秀的作品，如《囚徒》、《致大海》、《致凯恩》和《假如生活欺骗了你》等几十首抒情诗，叙事诗《努林伯爵》，历史剧《鲍里斯·戈都诺夫》，以及《叶甫盖尼·奥涅金》前六章。"
---
# Chinese RoBERTa-Base Model for QA
## Model description
用中文预料微调的QA模型.
## Overview
- **Language model**: RoBERTa-Base
- **Model size**: 400M
- **Language**: Chinese
## How to use
You can use the model directly with a pipeline for extractive question answering:
```python
>>> from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
>>> context = '卡利亚·基拔（，）生于英国汉默史密斯，是一名英格兰籍职业足球员，于2010年夏季约满离开母会阿仙奴。直到2005/06年，基拔通常在阿仙奴的青年后备队效力。他在首次在2005年11月29日的联赛杯赛事上场，并于12月7日，在一个欧洲联赛冠军杯比赛对阿积士，作为替代左后卫，入替受伤的劳伦。2006年7月21日阿仙奴宣布，将基拔出借卡迪夫城整个2006-07赛季，其后转借给修安联。2008年1月3日返回阿仙奴授予46号码。2008年2月11日，阿仙奴的英超联赛比赛中对布莱克本作为后备球员。但2008年7月10日，基拔被出借莱斯特城的一个赛季之久。2009年3月3日主场对-{zh-hans:斯托克港;zh-hk:史托港}-，开赛后仅两分钟，基拔的传中球「挞Q」却直入网角，是他个人首个入球。基拔在外借期间成为常规正选，整季上阵达39场及射入1球，协助莱斯特城赢取英甲联赛冠军及重返英冠。2009/10年上半季仅于两场英格兰联赛杯及一场无关痛痒的欧联分组赛上阵，将于季后约满的基拔获外借到英冠榜末球会彼德堡直到球季结束，期间上阵10场。2010年夏季基拔约满阿仙奴成为自由球员，仅为母会合共上阵10场，英超「升班马」黑池有意罗致，其后前往-{zh-hans:谢菲尔德联; zh-hk:锡菲联;}-参加试训，惟未有获得录用。'
>>> mode_name = 'liam168/qa-roberta-base-chinese-extractive'
>>> model = AutoModelForQuestionAnswering.from_pretrained(mode_name)
>>> tokenizer = AutoTokenizer.from_pretrained(mode_name)
>>> QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
>>> QA_input = {'question': "卡利亚·基拔的职业是什么?",'context': context}
>>> QA(QA_input)
{'score': 0.9999, 'start': 20, 'end': 31, 'answer': '一名英格兰籍职业足球员'}
```
## Contact
[email protected]
|
liam168/trans-opus-mt-zh-en | liam168 | 2021-07-16T03:34:38Z | 251 | 21 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-03-02T23:29:05Z | ---
language:
- en
- zh
tags:
- translation
widget:
- text: "我喜欢学习数据科学和机器学习。"
---
# liam168/trans-opus-mt-zh-en
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
## How to use
```python
>>> from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
>>> mode_name = 'liam168/trans-opus-mt-zh-en'
>>> model = AutoModelWithLMHead.from_pretrained(mode_name)
>>> tokenizer = AutoTokenizer.from_pretrained(mode_name)
>>> translation = pipeline("translation_zh_to_en", model=model, tokenizer=tokenizer)
>>> translation('我喜欢学习数据科学和机器学习。', max_length=400)
[{'translation_text': 'I like to study data science and machine learning.'}]
```
## Contact
[email protected]
|
flax-community/transformer-vae | flax-community | 2021-07-15T14:48:16Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # Transformer-VAE (flax) (WIP)
A Transformer-VAE made using flax.
Done as part of Huggingface community training ([see forum post](https://discuss.huggingface.co/t/train-a-vae-to-interpolate-on-english-sentences/7548)).
Builds on T5, using an autoencoder to convert it into an MMD-VAE.
[See training logs.](https://wandb.ai/fraser/flax-vae)
## ToDo
- [ ] Basic training script working. (Fraser + Theo)
- [ ] Add MMD loss (Theo)
- [ ] Save a wikipedia sentences dataset to Huggingface (see original https://github.com/ChunyuanLI/Optimus/blob/master/data/download_datasets.md) (Mina)
- [ ] Make a tokenizer using the OPTIMUS tokenized dataset.
- [ ] Train on the OPTIMUS wikipedia sentences dataset.
- [ ] Make Huggingface widget interpolating sentences! (???) https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects#how-to-build-a-demo
Optional ToDos:
- [ ] Add Funnel transformer encoder to FLAX (don't need weights).
- [ ] Train a Funnel-encoder + T5-decoder transformer VAE.
- [ ] Additional datasets:
- [ ] Poetry (https://www.gwern.net/GPT-2#data-the-project-gutenberg-poetry-corpus)
- [ ] 8-bit music (https://github.com/chrisdonahue/LakhNES)
## Setup
Follow all steps to install dependencies from https://cloud.google.com/tpu/docs/jax-quickstart-tpu-vm
- [ ] Find dataset storage site.
- [ ] Ask JAX team for dataset storage.
|
jambo/microsoftBio-renet | jambo | 2021-07-15T11:41:27Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:renet",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- renet
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-renet
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: renet
type: renet
metric:
name: Accuracy
type: accuracy
value: 0.8640646029609691
---
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-renet
A model for detecting gene disease associations from abstracts. The model classifies as 0 for no association, or 1 for some association.
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [RENET2](https://github.com/sujunhao/RENET2) dataset. Note that this considers only the abstract data, and not the full text information, from RENET2.
It achieves the following results on the evaluation set:
- Loss: 0.7226
- Precision: 0.7799
- Recall: 0.8211
- F1: 0.8
- Accuracy: 0.8641
- Auc: 0.9325
## Training procedure
The abstract dataset from RENET2 was split into 85% train, 15% evaluation being grouped by PMIDs and stratified by labels. That is, no data from the same PMID was seen in multiple both the training and the evaluation set.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.9.0.dev0
- Pytorch 1.10.0.dev20210630+cu113
- Datasets 1.8.0
- Tokenizers 0.10.3
|
huggingtweets/bladeecity-robber0540 | huggingtweets | 2021-07-15T06:48:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/bladeecity-robber0540/1626331680252/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406669132527976453/Sv0lEtmk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/822229503212666880/L4UutyTM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim & Combat Ballerina</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-robber0540</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Aim & Combat Ballerina.
| Data | Aim | Combat Ballerina |
| --- | --- | --- |
| Tweets downloaded | 1604 | 671 |
| Retweets | 314 | 66 |
| Short tweets | 487 | 303 |
| Tweets kept | 803 | 302 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uvtcfjv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bladeecity-robber0540's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36qst0l8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36qst0l8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bladeecity-robber0540')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/theisaiahw | huggingtweets | 2021-07-14T21:05:53Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/theisaiahw/1626296749614/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1388820869762322434/v3h5S7mu_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Isaiah Williams</div>
<div style="text-align: center; font-size: 14px;">@theisaiahw</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Isaiah Williams.
| Data | Isaiah Williams |
| --- | --- |
| Tweets downloaded | 620 |
| Retweets | 65 |
| Short tweets | 72 |
| Tweets kept | 483 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/336gn9be/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @theisaiahw's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ohqpafvm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ohqpafvm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/theisaiahw')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sergunow/movie-chat | sergunow | 2021-07-14T19:46:21Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"conversational",
"en",
"dataset:rick_and_morty",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
thumbnail:
tags:
- conversational
license: apache-2.0
datasets:
- rick_and_morty
metrics:
- perplexity
---
## Model description
Fine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty |
YusufSahin99/IFIS_ZORK_AI_HORROR | YusufSahin99 | 2021-07-14T14:11:24Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model_index:
- name: IFIS_ZORK_AI_HORROR
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IFIS_ZORK_AI_HORROR
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
cstorm125/wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa | cstorm125 | 2021-07-14T07:45:06Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"deberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=wangchan-deberta_v1-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--revision mlm@ckp-41100 \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16 \
--use_auth_token
``` |
cstorm125/wangchanberta-base-wiki-20210520-news-spm-finetune-qa | cstorm125 | 2021-07-14T07:35:27Z | 20 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# wangchanberta-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearchth/wangchanberta-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearchth/wangchanberta-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16
```
|
cstorm125/wangchanberta-base-att-spm-uncased-finetune-qa | cstorm125 | 2021-07-14T07:24:50Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# airesearch/wangchanberta-base-att-spm-uncased
Finetuning `airesearch/wangchanberta-base-att-spm-uncased` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearch/wangchanberta-base-att-spm-uncased
python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--lowercase \
--pad_on_right \
--fp16
``` |
airesearch/xlm-roberta-base-finetune-qa | airesearch | 2021-07-14T07:13:00Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# xlm-roberta-base-finetune-qa
Finetuning `xlm-roberta-base` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Train with:
```
export WANDB_PROJECT=wangchanberta-qa
export MODEL_NAME=xlm-roberta-base
python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--pad_on_right \
--fp16
```
|
andi611/roberta-base-ner-conll2003 | andi611 | 2021-07-14T00:25:37Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: roberta-base-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315.646
- eval_steps_per_second: 39.529
- epoch: 1.14
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | AIDA-UPM | 2021-07-13T14:12:45Z | 292 | 12 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"multilingual",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:04Z | ---
pipeline_tag: sentence-similarity
language: "multilingual"
tags:
- feature-extraction
- sentence-similarity
- transformers
- multilingual
---
# mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences.
<!--- Describe your model here -->
This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at [GitHub](https://github.com/Huertas97/Multilingual-STSB). The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below).
<!-- ## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
# It support several languages
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]
# The pooling technique is automatically detected (mean pooling)
model = SentenceTransformer('mstsb-paraphrase-multilingual-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
``` -->
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# We should define the proper pooling function: Mean pooling
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
model = AutoModel.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
Check the test results in the Semantic Textual Similarity Tasks. The 15 languages available at the [Multilingual STSB](https://github.com/Huertas97/Multilingual-STSB) have been combined into monolingual and cross-lingual tasks, giving a total of 31 tasks. Monolingual tasks have both sentences from the same language source (e.g., Ar-Ar, Es-Es), while cross-lingual tasks have two sentences, each in a different language being one of them English (e.g., en-ar, en-es).
Here we compare the average multilingual semantic textual similairty capabilities between the `paraphrase-multilingual-mpnet-base-v2` based model and the `mstsb-paraphrase-multilingual-mpnet-base-v2` fine-tuned model across the 31 tasks. It is worth noting that both models are multilingual, but the second model is adjusted with multilingual data for semantic similarity. The average of correlation coefficients is computed by transforming each correlation coefficient to a Fisher's z value, averaging them, and then back-transforming to a correlation coefficient.
| Model | Average Spearman Cosine Test |
|:---------------------------------------------:|:------------------------------:|
| mstsb-paraphrase-multilingual-mpnet-base-v2 | 0.835890 |
| paraphrase-multilingual-mpnet-base-v2 | 0.818896 |
<br>
The following tables breakdown the performance of `mstsb-paraphrase-multilingual-mpnet-base-v2` according to the different tasks. For the sake of readability tasks have been splitted into monolingual and cross-lingual tasks.
| Monolingual Task | Pearson Cosine test | Spearman Cosine test |
|:------------------:|:---------------------:|:-----------------------:|
| en;en | 0.868048310692506 | 0.8740170943535747 |
| ar;ar | 0.8267139454193487 | 0.8284459741532022 |
| cs;cs | 0.8466821720942157 | 0.8485417688803879 |
| de;de | 0.8517285961812183 | 0.8557680051557893 |
| es;es | 0.8519185309064691 | 0.8552243211580456 |
| fr;fr | 0.8430951067985064 | 0.8466614534379704 |
| hi;hi | 0.8178258630578092 | 0.8176462079184331 |
| it;it | 0.8475909574305637 | 0.8494216064459076 |
| ja;ja | 0.8435588859386477 | 0.8456031494178619 |
| nl;nl | 0.8486765104527032 | 0.8520856765262531 |
| pl;pl | 0.8407840177883407 | 0.8443070467300299 |
| pt;pt | 0.8534880178249296 | 0.8578544068829622 |
| ru;ru | 0.8390897585455678 | 0.8423041443534423 |
| tr;tr | 0.8382125451820572 | 0.8421587450058385 |
| zh-CN;zh-CN | 0.826233678946644 | 0.8248515460782744 |
| zh-TW;zh-TW | 0.8242683809675422 | 0.8235506799952028 |
<br>
| Cross-lingual Task | Pearson Cosine test | Spearman Cosine test |
|:--------------------:|:---------------------:|:-----------------------:|
| en;ar | 0.7990830340462535 | 0.7956792016468148 |
| en;cs | 0.8381274879061265 | 0.8388713450024455 |
| en;de | 0.8414439600928739 | 0.8441971698649943 |
| en;es | 0.8442337511356952 | 0.8445035292903559 |
| en;fr | 0.8378437644605063 | 0.8387903367907733 |
| en;hi | 0.7951955086055527 | 0.7905052217683244 |
| en;it | 0.8415686372978766 | 0.8419480899107785 |
| en;ja | 0.8094306665283388 | 0.8032512280936449 |
| en;nl | 0.8389526140129767 | 0.8409310421803277 |
| en;pl | 0.8261309163979578 | 0.825976253023656 |
| en;pt | 0.8475546209070765 | 0.8506606391790897 |
| en;ru | 0.8248514914263723 | 0.8224871183202255 |
| en;tr | 0.8191803661207868 | 0.8194200775744044 |
| en;zh-CN | 0.8147678083378249 | 0.8102089470690433 |
| en;zh-TW | 0.8107272160374955 | 0.8056129680510944 |
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 687 with parameters:
```
{'batch_size': 132, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 2,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 140,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
marefa-nlp/summarization-arabic-english-news | marefa-nlp | 2021-07-13T13:06:31Z | 62 | 4 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ------------
## Arabic and English News Summarization NLP Model
### About
This model is for summarizing news stories in short highlights for both Arabic and English tasks.
نموذج معرفي متخصص في تلخيص الأخبار العربية و الإنجليزية الى مجموعة من أهم النقاط
### Fine-Tuning
The model was finetuned using the [Arabic T5 Model](https://huggingface.co/bakrianoo/t5-arabic-large) which developed by [Abu Bakr Soliman](http://github.com/bakrianoo).
The primary summarization model also developed by the same developer.
### How to Use
- You can use this [Colab Notebook](https://colab.research.google.com/drive/1DWND1CAfCXD4OxrfmLBEaKeXhjGmYkod?usp=sharing) to test the model
1. Install [PyTorch](https://pytorch.org/)
2. Install the following Python packages
`$ pip3 install transformers==4.7.0 nltk==3.5 protobuf==3.15.3 sentencepiece==0.1.96`
3. Run this code
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
m_name = "marefa-nlp/summarization-arabic-english-news"
tokenizer = AutoTokenizer.from_pretrained(m_name)
model = AutoModelWithLMHead.from_pretrained(m_name).to(device)
def get_summary(text, tokenizer, model, device="cpu", num_beams=2):
if len(text.strip()) < 50:
return ["Please provide more longer text"]
text = "summarize: <paragraph> " + " <paragraph> ".join([ s.strip() for s in sent_tokenize(text) if s.strip() != ""]) + " </s>"
text = text.strip().replace("\n","")
tokenized_text = tokenizer.encode(text, return_tensors="pt").to(device)
summary_ids = model.generate(
tokenized_text,
max_length=512,
num_beams=num_beams,
repetition_penalty=1.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return [ s.strip() for s in output.split("<hl>") if s.strip() != "" ]
## Prepare Samples
samples = [
"""
قال المدافع الإيطالي ليوناردو بونوتشي إن منتخب بلاده ليس خائفا من مواجهة نظيره الانجليزي على أرضه في المباراة النهائية في بطولة يورو 2020 لكرة القدم، في حين وصف المدافع الانجليزي جون ستونز المباراة المرتقبة بأنها ستكون "أكثر تميزا".
وسوف تقام المباراة في استاد ويمبلي، شمال غربي لندن، يوم الأحد.
وتسعى إيطاليا لإحراز اللقب الأوروبي للمرة الثانية بعد فوزها به أول مرة عام 1968.
ولم يفز الفريق الانجليزي بهذا اللقب القاري من قبل. والبطولة الرئيسية الوحيدة التي فازت بها انجلترا هي كأس العالم عام 1966 الذي أقيمت مباراته النهائية في استاد ويمبلي.
""",
"""
On a night fraught with tension, Italy clinched its first major title for 15 years with a penalty shootout win over England in the Euro 2020 final.
Luke Shaw's goal inside the opening two minutes gave England a lead it looked like it would hold onto all night, before a goalmouth scramble midway through the second half allowed Leonardo Bonucci to poke home an equalizer for Italy.
For the remainder of the match, it felt as though extra-time and penalties were inevitable, as neither side seemed willing or brave enough to commit enough men forward to really trouble the opposing defenders.
England had suffered innumerable heartbreaks on penalties over the years and this time it was Italy's turn to inflict yet more pain on beleaguered English fans as Marcus Rashford, Jadon Sancho and Bukayo Saka all missed from the spot.
""",
]
## Get summaries
print("Original Article:", samples[0])
print("\n===========\nSummary: \n")
hls = get_summary(samples[0], tokenizer, model, device)
for hl in hls:
print("\t-", hl)
print("Original Article:", samples[1])
print("\n=========== \nSummary: \n")
hls = get_summary(samples[1], tokenizer, model, device)
for hl in hls:
print("\t-", hl)
```
Results
```
Original Article:
قال المدافع الإيطالي ليوناردو بونوتشي إن منتخب بلاده ليس خائفا من مواجهة نظيره الانجليزي على أرضه في المباراة النهائية في بطولة يورو 2020 لكرة القدم، في حين وصف المدافع الانجليزي جون ستونز المباراة المرتقبة بأنها ستكون "أكثر تميزا".
وسوف تقام المباراة في استاد ويمبلي، شمال غربي لندن، يوم الأحد.
وتسعى إيطاليا لإحراز اللقب الأوروبي للمرة الثانية بعد فوزها به أول مرة عام 1968.
ولم يفز الفريق الانجليزي بهذا اللقب القاري من قبل. والبطولة الرئيسية الوحيدة التي فازت بها انجلترا هي كأس العالم عام 1966 الذي أقيمت مباراته النهائية في استاد ويمبلي.
===========
Summary:
- وسوف تواجه إيطاليا إنجلترا في بطولة يورو 2020 لكرة القدم يوم الأحد.
- ستقام المباراة في استاد ويمبلي، شمال غربي لندن، يوم الأحد.
- ولم يفز الفريق الانجليزي بهذا اللقب القاري قبل.
```
```
Original Article:
On a night fraught with tension, Italy clinched its first major title for 15 years with a penalty shootout win over England in the Euro 2020 final.
Luke Shaw's goal inside the opening two minutes gave England a lead it looked like it would hold onto all night, before a goalmouth scramble midway through the second half allowed Leonardo Bonucci to poke home an equalizer for Italy.
For the remainder of the match it felt as though extra-time and penalties were inevitable, as neither side seemed willing or brave enough to commit enough men forward to really trouble the opposing defenders.
England had suffered innumerable heartbreaks on penalties over the years and this time it was Italy's turn to inflict yet more pain on beleaguered English fans as Marcus Rashford, Jadon Sancho and Bukayo Saka all missed from the spot.
===========
Summary:
- Luke Shaw's goal gave England a lead it looked like it would hold onto all night.
- Leonardo Bonucci scored the equalizer for Italy.
- Marcus Rashford, Jadon Sancho and Bukayo Saka all missed.
```
|
nateraw/test_model_a | nateraw | 2021-07-13T04:52:00Z | 128 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- image_folder
model_index:
- name: test_model_a
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_model_a
This model is a fine-tuned version of [lysandre/tiny-vit-random](https://huggingface.co/lysandre/tiny-vit-random) on the image_folder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 40
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/gpt2-als-demo | patrickvonplaten | 2021-07-12T14:20:19Z | 8 | 0 | transformers | [
"transformers",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | # roberta-base-als-demo
**roberta-base-als-demo** is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic language.
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Useful links
- [Community Week timeline](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104#summary-timeline-calendar-6)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Masked Language Modelling example scripts](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
- [Model Repository](https://huggingface.co/flax-community/roberta-base-als-demo)
|
EasthShin/Klue-CommonSense-model | EasthShin | 2021-07-12T10:01:36Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z |
#### Klue-bert base for Common Sense QA
#### Klue-CommonSense-model DEMO: [Ainize DEMO](https://main-klue-common-sense-qa-east-h-shin.endpoint.ainize.ai/)
#### Klue-CommonSense-model API: [Ainize API](https://ainize.ai/EastHShin/Klue-CommonSense_QA?branch=main)
### Overview
**Language model**: klue/bert-base
<br>
**Language**: Korean
<br>
**Downstream-task**: Extractive QA
<br>
**Training data**: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
<br>
**Eval data**: Common sense Data from [Mindslab](https://mindslab.ai:8080/kr/company)
<br>
**Code**: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Klue-CommonSense-workspace)
<br>
### Usage
### In Transformers
```
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EasthShin/Klue-CommonSense-model")
model = AutoModelForQuestionAnswering.from_pretrained("EasthShin/Klue-CommonSense-model")
context = "your context"
question = "your question"
encodings = tokenizer(context, question, max_length=512, truncation=True,
padding="max_length", return_token_type_ids=False)
encodings = {key: torch.tensor([val]) for key, val in encodings.items()}
input_ids = encodings["input_ids"]
attention_mask = encodings["attention_mask"]
pred = model(input_ids, attention_mask=attention_mask)
start_logits, end_logits = pred.start_logits, pred.end_logits
token_start_index, token_end_index = start_logits.argmax(dim=-1), end_logits.argmax(dim=-1)
pred_ids = input_ids[0][token_start_index: token_end_index + 1]
prediction = tokenizer.decode(pred_ids)
``` |
huggingtweets/pontifex_es | huggingtweets | 2021-07-12T08:26:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/pontifex_es/1626078305422/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/507819548834148352/jyx1JOS-_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Papa Francisco</div>
<div style="text-align: center; font-size: 14px;">@pontifex_es</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Papa Francisco.
| Data | Papa Francisco |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 45 |
| Tweets kept | 3205 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2a8o5bwd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pontifex_es's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3183nmsb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3183nmsb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/pontifex_es')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
srosy/distilbert-base-uncased-finetuned-ner | srosy | 2021-07-11T15:29:20Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9844313470062116
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0590
- Precision: 0.9266
- Recall: 0.9381
- F1: 0.9323
- Accuracy: 0.9844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0616 | 1.0 | 878 | 0.0604 | 0.9195 | 0.9370 | 0.9282 | 0.9833 |
| 0.0328 | 2.0 | 1756 | 0.0588 | 0.9258 | 0.9375 | 0.9316 | 0.9841 |
| 0.0246 | 3.0 | 2634 | 0.0590 | 0.9266 | 0.9381 | 0.9323 | 0.9844 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
|
ysharma/new-model-dummy | ysharma | 2021-07-11T11:51:02Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # Dummy model
This is just a dummy model. Copying bert-base-uncased model files over here. |
huggingtweets/aliceaeterna-clamtime-redpandasmash | huggingtweets | 2021-07-10T14:02:00Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/aliceaeterna-clamtime-redpandasmash/1625925715720/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1343482928014237696/51aKOINn_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408716131867713538/rg3HSZ5D_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1378382707625975812/vYek426__400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">che 💜 & clementine!!!! 𓃠 & 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻</div>
<div style="text-align: center; font-size: 14px;">@aliceaeterna-clamtime-redpandasmash</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from che 💜 & clementine!!!! 𓃠 & 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻.
| Data | che 💜 | clementine!!!! 𓃠 | 𝓡𝓮𝓭 𝓟𝓪𝓷𝓭𝓪'𝓼 𝓖𝓪𝓶𝓮 𝓒𝓸𝓻𝓷𝓮𝓻 |
| --- | --- | --- | --- |
| Tweets downloaded | 1587 | 3187 | 2492 |
| Retweets | 682 | 500 | 367 |
| Short tweets | 158 | 687 | 362 |
| Tweets kept | 747 | 2000 | 1763 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1814x6xo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aliceaeterna-clamtime-redpandasmash's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kvo9buwa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kvo9buwa/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aliceaeterna-clamtime-redpandasmash')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/cassandraautumn | huggingtweets | 2021-07-10T03:53:34Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/cassandraautumn/1625889209816/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1368065495816151041/PHixetcc_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Cassandra Autumn</div>
<div style="text-align: center; font-size: 14px;">@cassandraautumn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Cassandra Autumn.
| Data | Cassandra Autumn |
| --- | --- |
| Tweets downloaded | 583 |
| Retweets | 283 |
| Short tweets | 76 |
| Tweets kept | 224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1d6zyhom/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cassandraautumn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c2uc7mv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c2uc7mv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/cassandraautumn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/hochimeme1 | huggingtweets | 2021-07-09T20:06:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/hochimeme1/1625861211819/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1408277423498698752/aUTHbyW2_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Moe Chi Minh</div>
<div style="text-align: center; font-size: 14px;">@hochimeme1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Moe Chi Minh.
| Data | Moe Chi Minh |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 55 |
| Short tweets | 484 |
| Tweets kept | 2703 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21ljhxlm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hochimeme1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vctf4ad) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vctf4ad/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/hochimeme1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/robit-roberta-base-it | flax-community | 2021-07-09T16:47:58Z | 8 | 1 | transformers | [
"transformers",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # RobIt
**RobIt** is a RoBERTa-base model for Italian. It has been trained from scratch on the Italian portion of the OSCAR dataset using [Flax](https://github.com/google/flax), including training scripts.
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Team members
- Prateek Agrawal (prateekagrawal)
- Tanay Mehta (yotanay)
- Shreya Gupta (Sheyz-max)
- Ruchi Bhatia (ruchi798)
## Dataset :
[OSCAR](https://huggingface.co/datasets/oscar)
- config : **unshuffled_deduplicated_it**
- Size of downloaded dataset files: **26637.62 MB**
- Size of the generated dataset: **70661.48 MB**
- Total amount of disk used: **97299.10 MB**
## Useful links
- [Community Week timeline](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104#summary-timeline-calendar-6)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Community Week thread](https://discuss.huggingface.co/t/robit-pretrain-roberta-base-from-scratch-in-italian/7564)
- [Community Week channel](https://discord.gg/NTyQNUNs)
- [Masked Language Modelling example scripts](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
- [Model Repository](https://huggingface.co/flax-community/robit-roberta-base-it/)
|
Prim9000/try | Prim9000 | 2021-07-09T14:57:22Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z | https://github.com/Prim9000/Thai_TTS |
flax-community/roberta-base-als | flax-community | 2021-07-09T12:18:18Z | 4 | 0 | transformers | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | This project pretrains a [`roberta-base`](https://huggingface.co/roberta-base) on the *Alemannic* (`als`) data subset of the [OSCAR](https://oscar-corpus.com/) corpus in JAX/Flax.
We will be using the masked-language modeling loss for pretraining. |
Kao/samyarn-bert-base-multilingual-cased | Kao | 2021-07-09T08:55:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | samyarn-bert-base-multilingual-cased
kao |
Alireza1044/bert_classification_lm | Alireza1044 | 2021-07-09T08:50:58Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | A simple model trained on dialogues of characters in NBC series, `The Office`. The model can do a binary classification between `Michael Scott` and `Dwight Shrute`'s dialogues.
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-c3ow" colspan="2">Label Definitions</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-c3ow">Label 0</td>
<td class="tg-c3ow">Michael</td>
</tr>
<tr>
<td class="tg-c3ow">Label 1</td>
<td class="tg-c3ow">Dwight</td>
</tr>
</tbody>
</table> |
huggingtweets/dbdevletbahceli | huggingtweets | 2021-07-09T07:53:26Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/dbdevletbahceli/1625817202615/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1163922647/db002_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Devlet Bahçeli</div>
<div style="text-align: center; font-size: 14px;">@dbdevletbahceli</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Devlet Bahçeli.
| Data | Devlet Bahçeli |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 0 |
| Short tweets | 19 |
| Tweets kept | 3181 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ni0ttu3d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dbdevletbahceli's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ois198tw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ois198tw/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dbdevletbahceli')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ThomasNLG/t5-qg_squad1-en | ThomasNLG | 2021-07-09T07:45:35Z | 886 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qg",
"question",
"generation",
"SQuAD",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- qg
- question
- generation
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad
model-index:
- name: t5-qg_squad1-en
results:
- task:
name: Question Generation
type: Text2Text-Generation
widget:
- text: "sv1 </s> Louis 14 </s> Louis 14 was a French King."
---
# t5-qg_squad1-en
## Model description
This model is a *Question Generation* model based on T5-small.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QG only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_squad1-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_squad1-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "sv1 </s> {ANSWER} </s> {CONTEXT}"`
## Training data
The model was trained on SQuAD.
### Citation info
```bibtex
@article{scialom2020QuestEval,
title={QuestEval: Summarization Asks for Fact-based Evaluation},
author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
journal={arXiv preprint arXiv:2103.12693},
year={2021}
}
``` |
ThomasNLG/t5-weighter_cnndm-en | ThomasNLG | 2021-07-09T07:45:02Z | 62 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"classification",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad",
"dataset:cnndm",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- qa
- classification
- question
- answering
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad
- cnndm
model-index:
- name: t5-weighter_cnndm-en
results:
- task:
name: Classification
type: Question Weighter
widget:
- text: "a Buckingham Palace guard </s> Who felt on a manhole? </s> This is the embarrassing moment a Buckingham Palace guard slipped and fell on a manhole cover in front of hundreds of shocked tourists as he took up position in his sentry box. [...] The Guard comprises two detachments, one each for Buckingham Palace and St James’s Palace, under the command of the Captain of The Queen’s Guard."
---
# t5-weighter_cnndm-en
## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-weighter_cnndm-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-weighter_cnndm-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "{ANSWER} </s> {QUESTION} </s> {CONTEXT}"`
## Training data
The model was trained on synthetic data as described in [Questeval: Summarization asks for fact-based evaluation](https://arxiv.org/abs/2103.12693).
### Citation info
```bibtex
@article{scialom2021questeval,
title={Questeval: Summarization asks for fact-based evaluation},
author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
journal={arXiv preprint arXiv:2103.12693},
year={2021}
}
``` |
ThomasNLG/t5-qa_squad2neg-en | ThomasNLG | 2021-07-09T07:44:39Z | 797 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad_v2",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- qa
- question
- answering
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qa_squad2neg-en
results:
- task:
name: Question Answering
type: extractive-qa
widget:
- text: "Who was Louis 14? </s> Louis 14 was a French King."
---
# t5-qa_squad2neg-en
## Model description
This model is a *Question Answering* model based on T5-small.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_squad2neg-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_squad2neg-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "{QUESTION} </s> {CONTEXT}"`
## Training data
The model was trained on:
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable* to help the model learning when the question is not answerable given the context. For more details, see the [paper](https://arxiv.org/abs/2103.12693).
### Citation info
```bibtex
@article{scialom2020QuestEval,
title={QuestEval: Summarization Asks for Fact-based Evaluation},
author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
journal={arXiv preprint arXiv:2103.12693},
year={2021}
}
```
|
henryu-lin/t5-large-samsum-deepspeed | henryu-lin | 2021-07-08T09:13:46Z | 16 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"azureml",
"summarization",
"deepspeed",
"en",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-02T23:29:05Z |
---
language: en
tags:
- azureml
- t5
- summarization
- deepspeed
license: apache-2.0
datasets:
- samsum
model-index:
- name: t5-large-samsum-deepspeed
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
type: samsum
widget:
- text: |
Kevin: Hey man, are you excited to watch Finding Nemo tonight?
Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?
Kevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.
Kevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.
Henry: Nice, I'm really looking forward to seeing them again.
---
## `t5-large-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-large` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, major updates coming soon: 7/6/21~7/9/21)*
## Resource Usage
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
| key | value |
| --- | ----- |
| AzureML SKU | ND40rs_v2 (8 X V100 32GB) |
| Region | US West 2 |
| Run Duration | 12m 47.13s |
| Compute Cost (LowPriority/Dedicated) | $0.94/$4.69 (USD) |
| Average CPU Utilization | 51.2% |
| Average GPU Utilization | 42.0% |
| GPU Memory Usage (Avg/Peak) | 24.85/28.79 (GB) |
| Total GPU Energy Usage | 670.38 (kJ) |
*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: https://github.com/mlco2/codecarbon
| key | value |
| --- | ----- |
| timestamp | 2021-07-08T06:29:27 |
| duration | 515.5018835067749 |
| emissions | 0.043562840982919106 |
| energy_consumed | 0.14638051405550773 |
| country_name | USA |
| region | Washington |
| cloud_provider | azure |
| cloud_region | westus2 |
## Hyperparameters
```yaml
fp16: True
per device batch size: 8
effective batch size: 64
epoch: 3.0
learning rate: 1e-4
weight decay: 0.1
seed: 1
```
*Same `per device batch size` for evaluations
### DeepSpeed
Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none`
```json
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 1300000000,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1300000000,
"contiguous_gradients": true
}
```
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-large-samsum-deepspeed")
conversation = '''Kevin: Hey man, are you excited to watch Finding Nemo tonight?
Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?
Kevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.
Kevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.
Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(conversation)
```
## Results
| ROUGE | Score |
| ----- | ----- |
| eval_rouge1 | 53.0823 |
| eval_rouge2 | 28.7097 |
| eval_rougeL | 43.939 |
| eval_rougeLsum | 49.067 |
| predict_rouge1 | 51.6716 |
| predict_rouge2 | 26.5372 |
| predict_rougeL | 42.9681 |
| predict_rougeLsum | 47.4084 |
| Metric | Value |
| ------ | ----- |
| eval_gen_len | 26.4071 |
| predict_gen_len | 25.9451 |
| train_loss | 1.3212629926497115 |
| eval_loss | 1.23828125 |
| predict_loss | 1.2333984375 |
| train_runtime | 515.2198 |
| train_samples | 14732 |
| train_samples_per_second | 85.781 |
| train_steps_per_second | 1.345 |
| eval_runtime | 61.275 |
| eval_samples | 818 |
| eval_samples_per_second | 13.35 |
| eval_steps_per_second | 0.212 |
| predict_runtime | 63.3732 |
| predict_samples | 819 |
| predict_samples_per_second | 12.923 |
| predict_steps_per_second | 0.205 |
| total_steps | 693 |
| total_flos | 7.20140924616704e+16 |
|
liam168/c2-roberta-base-finetuned-dianping-chinese | liam168 | 2021-07-08T01:50:53Z | 174 | 23 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: zh
widget:
- text: "我喜欢下雨。"
- text: "我讨厌他。"
---
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- **Language model**: BertForSequenceClassification
- **Model size**: 410M
- **Language**: Chinese
## Example
```python
>>> from transformers import AutoModelForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c2-roberta-base-finetuned-dianping-chinese"
>>> class_num = 2
>>> ts_texts = ["我喜欢下雨。", "我讨厌他."]
>>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'positive', 'score': 0.9973447918891907}]
[{'label': 'negative', 'score': 0.9972558617591858}]
```
|
tdopierre/ProtAugment-ParaphraseGenerator | tdopierre | 2021-07-07T14:15:07Z | 4 | 5 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Paraphase Generation",
"Data Augmentation",
"en",
"dataset:Quora",
"dataset:MSR",
"dataset:Google-PAWS",
"arxiv:2105.12995",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: "en"
tags:
- Paraphase Generation
- Data Augmentation
datasets:
- Quora
- MSR
- Google-PAWS
---
[](https://arxiv.org/abs/2105.12995)
This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: MSR, Quora, Google-PAWS.
We use this model in our ACL'21 Paper ["PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning"](https://arxiv.org/abs/2105.12995)
Jointly used with generation constraints, this model allows to generate diverse paraphrases. We use those paraphrases as a data augmentation technique to further boosts a classification model's generalization capability. Feel free to play with the [code](https://github.com/tdopierre/ProtAugment)!
If you use this model, please consider citing our paper.
```
@article{Dopierre2021ProtAugmentUD,
title={ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning},
author={Thomas Dopierre and C. Gravier and Wilfried Logerais},
journal={ArXiv},
year={2021},
volume={abs/2105.12995}
}
```
|
shreeshaaithal/DialoGPT-small-Michael-Scott | shreeshaaithal | 2021-07-07T11:56:25Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on WhatsApp chats
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
feel free to ask me questions on discord server [discord server](https://discord.gg/Gqhje8Z7DX)
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("harrydonni/DialoGPT-small-Michael-Scott")
model = AutoModelWithLMHead.from_pretrained("harrydonni/DialoGPT-small-Michael-Scott")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("Michael: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
this is done by shreesha thank you...... |
andi611/bert-base-uncased-ner-conll2003 | andi611 | 2021-07-07T09:31:59Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-base-uncased-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.19881805328292054
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1258
- Precision: 0.0269
- Recall: 0.1379
- F1: 0.0451
- Accuracy: 0.1988
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 4 | 2.1296 | 0.0270 | 0.1389 | 0.0452 | 0.1942 |
| No log | 2.0 | 8 | 2.1258 | 0.0269 | 0.1379 | 0.0451 | 0.1988 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
shreeshaaithal/whatsapp-medium-bot-2 | shreeshaaithal | 2021-07-07T06:28:15Z | 7 | 2 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on WhatsApp chats
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
feel free to ask me questions on discord server [discord server](https://discord.gg/Gqhje8Z7DX)
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("harrydonni/whatsapp-medium-bot-2")
model = AutoModelWithLMHead.from_pretrained("harrydonni/whatsapp-medium-bot-2")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("Messi: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
this is done by shreesha thank you...... |
minsik-oh/dummy-model | minsik-oh | 2021-07-07T05:58:51Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # Dummy Model
This be a dummmmmy |
byeongal/bart-base | byeongal | 2021-07-07T05:58:29Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
language: en
tags:
- bart
---
# BART base model for Teachable NLP
- This model forked from [bart-base](https://huggingface.co/facebook/bart-base) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
https://github.com/pytorch/fairseq/tree/master/examples/bart
|
liam168/c4-zh-distilbert-base-uncased | liam168 | 2021-07-07T03:21:34Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"exbert",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: zh
tags:
- exbert
license: apache-2.0
widget:
- text: "女人做得越纯粹，皮肤和身材就越好"
- text: "我喜欢篮球"
---
# liam168/c4-zh-distilbert-base-uncased
## Model description
用 ["女性","体育","文学","校园"]4类数据训练的分类模型。
## Overview
- **Language model**: DistilBERT
- **Model size**: 280M
- **Language**: Chinese
## Example
```python
>>> from transformers import DistilBertForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c4-zh-distilbert-base-uncased"
>>> class_num = 4
>>> ts_texts = ["女人做得越纯粹，皮肤和身材就越好", "我喜欢篮球"]
>>> model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'Female', 'score': 0.9137857556343079}]
[{'label': 'Sports', 'score': 0.8206522464752197}]
```
|
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